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\n \n\n \n \n \n \n \n \n Heterogeneous Catalysis in Grammar School.\n \n \n \n \n\n\n \n Margraf, J. T.; Ulissi, Z. W.; Jung, Y.; and Reuter, K.\n\n\n \n\n\n\n The Journal of Physical Chemistry C, 126(6): 2931-2936. 2022.\n \n\n\n\n
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@article{doi:10.1021/acs.jpcc.1c10285,\nauthor = {Margraf, Johannes T. and Ulissi, Zachary W. and Jung, Yousung and Reuter, Karsten},\ntitle = {Heterogeneous Catalysis in Grammar School},\njournal = {The Journal of Physical Chemistry C},\nvolume = {126},\nnumber = {6},\npages = {2931-2936},\nyear = {2022},\ndoi = {10.1021/acs.jpcc.1c10285},\n\nURL = { \n        https://doi.org/10.1021/acs.jpcc.1c10285\n    \n},\neprint = { \n        https://doi.org/10.1021/acs.jpcc.1c10285\n    \n}\n\n}\n\n

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\n \n\n \n \n \n \n \n \n How Do Graph Networks Generalize to Large and Diverse Molecular Systems?.\n \n \n \n \n\n\n \n Gasteiger, J.; Shuaibi, M.; Sriram, A.; Günnemann, S.; Ulissi, Z.; Zitnick, C. L.; and Das, A.\n\n\n \n\n\n\n arXiv preprint arXiv:2204.02782. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"How$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{gasteiger2022how,\n  title={How Do Graph Networks Generalize to Large and Diverse Molecular Systems?},\n  author={Johannes Gasteiger and  Muhammed Shuaibi and Anuroop Sriram and Stephan Günnemann and Zachary Ulissi and C. Lawrence Zitnick and Abhishek Das},\n  journal={arXiv preprint arXiv:2204.02782},\n  url={https://arxiv.org/abs/2204.02782},\n  doi={https://doi.org/10.48550/arXiv.2204.02782},\n  year={2022}\n}\n\n

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\n \n\n \n \n \n \n \n \n FINETUNA: Fine-tuning Accelerated Molecular Simulations.\n \n \n \n \n\n\n \n Musielewicz, J.; Wang, X.; Tian, T.; and Ulissi, Z.\n\n\n \n\n\n\n arXiv preprint arXiv:2205.01223. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"FINETUNA:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{musielewicz2022finetuna,\n  title={FINETUNA: Fine-tuning Accelerated Molecular Simulations},\n  author={Musielewicz, Joseph and Wang, Xiaoxiao and Tian, Tian and Ulissi, Zachary},\n  journal={arXiv preprint arXiv:2205.01223},\n  doi={10.48550/arXiv.2205.01223},\n  url={https://arxiv.org/abs/2205.01223},\n  year={2022}\n}\n\n

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\n \n\n \n \n \n \n \n \n Transfer Learning using Attentions across Atomic Systems with Graph Neural Networks (TAAG).\n \n \n \n \n\n\n \n Kolluru, A.; Shoghi, N.; Shuaibi, M.; Goyal, S.; Das, A.; Zitnick, L.; and Ulissi, Z. W\n\n\n \n\n\n\n The Journal of Chemical Physics, 0(ja): -. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Transfer$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{doi:10.1063/5.0088019,\nauthor = {Kolluru,Adeesh  and Shoghi,Nima  and Shuaibi,Muhammed  and Goyal,Siddharth  and Das,Abhishek  and Zitnick,Lawrence  and Ulissi,Zachary W },\ntitle = {Transfer Learning using Attentions across Atomic Systems with Graph Neural Networks (TAAG)},\njournal = {The Journal of Chemical Physics},\nvolume = {0},\nnumber = {ja},\npages = {-},\nyear = {2022},\ndoi = {10.1063/5.0088019},\n\nURL = { \n        https://doi.org/10.1063/5.0088019\n    \n},\neprint = { \n        https://doi.org/10.1063/5.0088019\n    \n}\n\n}\n\n

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\n \n\n \n \n \n \n \n \n Open Challenges in Developing Generalizable Large Scale Machine Learning Models for Catalyst Discovery.\n \n \n \n \n\n\n \n Kolluru, A.; Shuaibi, M.; Palizhati, A.; Shoghi, N.; Das, A.; Wood, B.; Zitnick, C. L.; Kitchin, J. R; and Ulissi, Z. W\n\n\n \n\n\n\n 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Open$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 13 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@misc{https://doi.org/10.48550/arxiv.2206.02005,\n  doi = {10.48550/ARXIV.2206.02005},\n  \n  url = {https://arxiv.org/abs/2206.02005},\n  \n  author = {Kolluru, Adeesh and Shuaibi, Muhammed and Palizhati, Aini and Shoghi, Nima and Das, Abhishek and Wood, Brandon and Zitnick, C. Lawrence and Kitchin, John R and Ulissi, Zachary W},\n  \n  keywords = {Chemical Physics (physics.chem-ph), Materials Science (cond-mat.mtrl-sci), FOS: Physical sciences, FOS: Physical sciences},\n  \n  title = {Open Challenges in Developing Generalizable Large Scale Machine Learning Models for Catalyst Discovery},\n  \n  publisher = {arXiv},\n  \n  year = {2022},\n  \n  copyright = {Creative Commons Attribution 4.0 International}\n}\n\n\n

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\n \n\n \n \n \n \n \n Hydrogen Adsorption Energy Necessary but Not Sufficient for HER Catalysis: Connecting Machine-Learned Descriptors with High-Throughput Experimental Catalysis over Bimetallic Nanoparticles.\n \n \n \n\n\n \n Broderick, K.; Lopato, E.; Wander, B.; Bernhard, S.; Kitchin, J.; and Ulissi, Z.\n\n\n \n\n\n\n chemrxiv preprint. 6 2022.\n \n\n\n\n
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@article{broderick2022hydrogen,\n  title={Hydrogen Adsorption Energy Necessary but Not Sufficient for HER Catalysis: Connecting Machine-Learned Descriptors with High-Throughput Experimental Catalysis over Bimetallic Nanoparticles},\n  author={Broderick, Kirby and Lopato, Eric and Wander, Brook and Bernhard, Stefan and Kitchin, John and Ulissi, Zachary},\n  journal = {chemrxiv preprint},\n  doi = {https://doi.org/10.26434/chemrxiv-2022-fkj67},\n  month = {6},\n  year={2022}\n}\n\n

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\n \n\n \n \n \n \n \n \n Spherical Channels for Modeling Atomic Interactions.\n \n \n \n \n\n\n \n Zitnick, C. L.; Das, A.; Kolluru, A.; Lan, J.; Shuaibi, M.; Sriram, A.; Ulissi, Z.; and Wood, B.\n\n\n \n\n\n\n . 6 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Spherical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{https://doi.org/10.48550/arxiv.2206.14331,\n  doi = {10.48550/ARXIV.2206.14331},\n  \n  url = {https://arxiv.org/abs/2206.14331},\n  \n  author = {Zitnick, C. Lawrence and Das, Abhishek and Kolluru, Adeesh and Lan, Janice and Shuaibi, Muhammed and Sriram, Anuroop and Ulissi, Zachary and Wood, Brandon},\n  \n  keywords = {Chemical Physics (physics.chem-ph), Computational Engineering, Finance, and Science (cs.CE), Machine Learning (cs.LG), Computational Physics (physics.comp-ph), FOS: Physical sciences, FOS: Physical sciences, FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.6; J.2},\n  \n  title = {Spherical Channels for Modeling Atomic Interactions},\n  \n  publisher = {arXiv},\n  month = {6},\n  year = {2022},\n  \n  copyright = {arXiv.org perpetual, non-exclusive license}\n}\n\n

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\n \n\n \n \n \n \n \n Site Geometry as a Descriptor for Catalyst Selectivity in Intermetallics.\n \n \n \n\n\n \n Sharma, U.; Nguyen, A.; Janik, M. J.; and Ulissi, Z.\n\n\n \n\n\n\n Preprint available at SSRN 4145497. 2022.\n \n\n\n\n
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@article{sharma4145497site,\n  title={Site Geometry as a Descriptor for Catalyst Selectivity in Intermetallics},\n  author={Sharma, Unnatti and Nguyen, Angela and Janik, Michael John and Ulissi, Zachary},\n  journal={Preprint available at SSRN 4145497},\n  year = {2022}\n}\n\n

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\n \n\n \n \n \n \n \n \n The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis.\n \n \n \n \n\n\n \n Tran, R.; Lan, J.; Shuaibi, M.; Goyal, S.; Wood, B. M.; Das, A.; Heras-Domingo, J.; Kolluru, A.; Rizvi, A.; Shoghi, N.; Sriram, A.; Ulissi, Z.; and Zitnick, C. L.\n\n\n \n\n\n\n arXiv. 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{https://doi.org/10.48550/arxiv.2206.08917,\n  doi = {10.48550/ARXIV.2206.08917},\n  \n  url = {https://arxiv.org/abs/2206.08917},\n  \n  author = {Tran, Richard and Lan, Janice and Shuaibi, Muhammed and Goyal, Siddharth and Wood, Brandon M. and Das, Abhishek and Heras-Domingo, Javier and Kolluru, Adeesh and Rizvi, Ammar and Shoghi, Nima and Sriram, Anuroop and Ulissi, Zachary and Zitnick, C. Lawrence},\n  \n  keywords = {Materials Science (cond-mat.mtrl-sci), Machine Learning (cs.LG), Computational Physics (physics.comp-ph), FOS: Physical sciences, FOS: Physical sciences, FOS: Computer and information sciences, FOS: Computer and information sciences},\n  \n  title = {The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis},\n  \n  journal = {arXiv},\n  \n  year = {2022},\n  \n  copyright = {arXiv.org perpetual, non-exclusive license}\n}\n\n

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\n \n 2021\n \n \n (6)\n \n \n

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\n \n\n \n \n \n \n \n \n Open Catalyst 2020 (OC20) Dataset and Community Challenges.\n \n \n \n \n\n\n \n Chanussot, L.; Das, A.; Goyal, S.; Lavril, T.; Shuaibi, M.; Riviere, M.; Tran, K.; Heras-Domingo, J.; Ho, C.; Hu, W.; Palizhati, A.; Sriram, A.; Wood, B.; Yoon, J.; Parikh, D.; Zitnick, C. L.; and Ulissi, Z.\n\n\n \n\n\n\n ACS Catalysis, 11(10): 6059-6072. 4 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Open$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{doi:10.1021/acscatal.0c04525,\nauthor = {Chanussot, Lowik and Das, Abhishek and Goyal, Siddharth and Lavril, Thibaut and Shuaibi, Muhammed and Riviere, Morgane and Tran, Kevin and Heras-Domingo, Javier and Ho, Caleb and Hu, Weihua and Palizhati, Aini and Sriram, Anuroop and Wood, Brandon and Yoon, Junwoong and Parikh, Devi and Zitnick, C. Lawrence and Ulissi, Zachary},\ntitle = {Open Catalyst 2020 (OC20) Dataset and Community Challenges},\njournal = {ACS Catalysis},\nvolume = {11},\nnumber = {10},\npages = {6059-6072},\nyear = {2021},\nmonth={4},\ndoi = {10.1021/acscatal.0c04525},\n\nURL = { \n        https://doi.org/10.1021/acscatal.0c04525\n    \n},\neprint = { \n        https://doi.org/10.1021/acscatal.0c04525\n    \n}\n\n}\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n \n Efficient Discovery of Active, Selective, and Stable Catalysts for Electrochemical H$_2$O$_2$ Synthesis through Active Motif Screening.\n \n \n \n \n\n\n \n Back, S.; Na, J.; and Ulissi, Z. W.\n\n\n \n\n\n\n ACS Catalysis, 11(5): 2483-2491. 2 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Efficient$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{doi:10.1021/acscatal.0c05494,\nauthor = {Back, Seoin and Na, Jonggeol and Ulissi, Zachary W.},\ntitle = {Efficient Discovery of Active, Selective, and Stable Catalysts for Electrochemical H$_2$O$_2$ Synthesis through Active Motif Screening},\njournal = {ACS Catalysis},\nvolume = {11},\nnumber = {5},\nmonth={2},\npages = {2483-2491},\nyear = {2021},\ndoi = {10.1021/acscatal.0c05494},\n\nURL = { \n        https://doi.org/10.1021/acscatal.0c05494\n    \n},\neprint = { \n        https://doi.org/10.1021/acscatal.0c05494\n    \n}\n\n}\n\n\n

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\n \n\n \n \n \n \n \n Computational catalyst discovery: Active classification through myopic multiscale sampling.\n \n \n \n\n\n \n Tran, K.; Neiswanger, W.; Broderick, K.; Xing, E.; Schneider, J.; and Ulissi, Z. W\n\n\n \n\n\n\n The Journal of Chemical Physics, 154(12): 124118. 2021.\n \n\n\n\n
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@article{tran2021computational,\n  title={Computational catalyst discovery: Active classification through myopic multiscale sampling},\n  author={Tran, Kevin and Neiswanger, Willie and Broderick, Kirby and Xing, Eric and Schneider, Jeff and Ulissi, Zachary W},\n  journal={The Journal of Chemical Physics},\n  volume={154},\n  number={12},\n  pages={124118},\n  year={2021},\n  publisher={AIP Publishing LLC}\n}\n\n\n

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\n \n\n \n \n \n \n \n \n Elimination of Multidrug-Resistant Bacteria by Transition Metal Dichalcogenides Encapsulated by Synthetic Single-Stranded DNA.\n \n \n \n \n\n\n \n Debnath, A.; Saha, S.; Li, D. O.; Chu, X. S.; Ulissi, Z. W.; Green, A. A.; and Wang, Q. H.\n\n\n \n\n\n\n ACS Applied Materials & Interfaces, 13(7): 8082-8094. 2021.\n PMID: 33570927\n\n\n\n
\n\n\n\n \n \n $\"Elimination$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 14 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{doi:10.1021/acsami.0c22941,\nauthor = {Debnath, Abhishek and Saha, Sanchari and Li, Duo O. and Chu, Ximo S. and Ulissi, Zachary W. and Green, Alexander A. and Wang, Qing Hua},\ntitle = {Elimination of Multidrug-Resistant Bacteria by Transition Metal Dichalcogenides Encapsulated by Synthetic Single-Stranded DNA},\njournal = {ACS Applied Materials \\& Interfaces},\nvolume = {13},\nnumber = {7},\npages = {8082-8094},\nyear = {2021},\ndoi = {10.1021/acsami.0c22941},\n    note ={PMID: 33570927},\n\nURL = { \n        https://doi.org/10.1021/acsami.0c22941\n    \n},\neprint = { \n        https://doi.org/10.1021/acsami.0c22941\n    \n}\n\n}\n\n\n\n

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\n \n\n \n \n \n \n \n Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy.\n \n \n \n\n\n \n Yoon, J.; Cao, Z.; Raju, R. K; Wang, Y.; Burnley, R.; Gellman, A. J; Farimani, A. B.; and Ulissi, Z. W\n\n\n \n\n\n\n Machine Learning: Science and Technology, 2(4): 045018. 2021.\n \n\n\n\n
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@article{yoon2021deep,\n  title={Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy},\n  author={Yoon, Junwoong and Cao, Zhonglin and Raju, Rajesh K and Wang, Yuyang and Burnley, Robert and Gellman, Andrew J and Farimani, Amir Barati and Ulissi, Zachary W},\n  journal={Machine Learning: Science and Technology},\n  volume={2},\n  number={4},\n  pages={045018},\n  year={2021},\n  publisher={IOP Publishing}\n}\n\n

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\n \n\n \n \n \n \n \n \n Rotation Invariant Graph Neural Networks using Spin Convolutions.\n \n \n \n \n\n\n \n Shuaibi, M.; Kolluru, A.; Das, A.; Grover, A.; Sriram, A.; Ulissi, Z.; and Zitnick, C L.\n\n\n \n\n\n\n arXiv preprint arXiv:2106.09575. 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Rotation$ Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{shuaibi2021rotation,\n  title={Rotation Invariant Graph Neural Networks using Spin Convolutions},\n  author={Shuaibi, Muhammed and Kolluru, Adeesh and Das, Abhishek and Grover, Aditya and Sriram, Anuroop and Ulissi, Zachary and Zitnick, C Lawrence},\n  journal={arXiv preprint arXiv:2106.09575},\n  url={https://arxiv.org/abs/2106.09575},\n  year={2021}\n}\n\n

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\n \n 2020\n \n \n (11)\n \n \n

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\n \n\n \n \n \n \n \n \n Capturing Structural Transitions in Surfactant Adsorption Isotherms at Solid/Solution Interfaces.\n \n \n \n \n\n\n \n Yoon, J.; and Ulissi, Z. W.\n\n\n \n\n\n\n Langmuir, 36(3): 819-826. 1 2020.\n PMID: 31891511\n\n\n\n
\n\n\n\n \n \n $\"Capturing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 29 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{doi:10.1021/acs.langmuir.9b02235,\nauthor = {Yoon, Junwoong and Ulissi, Zachary W.},\ntitle = {Capturing Structural Transitions in Surfactant Adsorption Isotherms at Solid/Solution Interfaces},\njournal = {Langmuir},\nvolume = {36},\nnumber = {3},\npages = {819-826},\nyear = {2020},\nmonth={1},\ndoi = {10.1021/acs.langmuir.9b02235},\n    note ={PMID: 31891511},\n\nURL = { \n        https://doi.org/10.1021/acs.langmuir.9b02235\n    \n},\neprint = { \n        https://doi.org/10.1021/acs.langmuir.9b02235\n    \n}\n\n}\n\n\n\n\n

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\n \n\n \n \n \n \n \n Methods for comparing uncertainty quantifications for material property predictions.\n \n \n \n\n\n \n Tran, K.; Neiswanger, W.; Yoon, J.; Zhang, Q.; Xing, E.; and Ulissi, Z. W\n\n\n \n\n\n\n Machine Learning: Science and Technology, 1(2): 025006. 5 2020.\n \n\n\n\n
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@article{tran2020methods,\n  title={Methods for comparing uncertainty quantifications for material property predictions},\n  author={Tran, Kevin and Neiswanger, Willie and Yoon, Junwoong and Zhang, Qingyang and Xing, Eric and Ulissi, Zachary W},\n  journal={Machine Learning: Science and Technology},\n  volume={1},\n  number={2},\n  pages={025006},\n  year={2020},\n  month={5},\n  publisher={IOP Publishing}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n Parallelized Screening of Characterized and DFT-Modeled Bimetallic Colloidal Cocatalysts for Photocatalytic Hydrogen Evolution.\n \n \n \n\n\n \n Lopato, E. M; Eikey, E. A; Simon, Z. C; Back, S.; Tran, K.; Lewis, J.; Kowalewski, J. F; Yazdi, S.; Kitchin, J. R; Ulissi, Z. W; and others\n\n\n \n\n\n\n ACS Catalysis, 10(7): 4244–4252. 3 2020.\n \n\n\n\n
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@article{lopato2020parallelized,\n  title={Parallelized Screening of Characterized and DFT-Modeled Bimetallic Colloidal Cocatalysts for Photocatalytic Hydrogen Evolution},\n  author={Lopato, Eric M and Eikey, Emily A and Simon, Zoe C and Back, Seoin and Tran, Kevin and Lewis, Jacqueline and Kowalewski, Jakub F and Yazdi, Sadegh and Kitchin, John R and Ulissi, Zachary W and others},\n  journal={ACS Catalysis},\n  volume={10},\n  number={7},\n  month={3},\n  pages={4244--4252},\n  year={2020},\n  publisher={ACS Publications}\n}\n\n\n\n

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\n \n\n \n \n \n \n \n Computational Notebooks in Chemical Engineering Curricula.\n \n \n \n\n\n \n Verrett, J.; Boukouvala, F.; Dowling, A.; Ulissi, Z.; and Zavala, V.\n\n\n \n\n\n\n Chemical Engineering Education, 54(3): 143–150. 7 2020.\n \n\n\n\n
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@article{verrett2020computational,\n  title={Computational Notebooks in Chemical Engineering Curricula},\n  author={Verrett, Jonathan and Boukouvala, Fani and Dowling, Alexander and Ulissi, Zachary and Zavala, Victor},\n  journal={Chemical Engineering Education},\n  volume={54},\n  number={3},\n  pages={143--150},\n  month={7},\n  year={2020}\n}\n\n

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\n \n\n \n \n \n \n \n \n Accelerated discovery of CO2 electrocatalysts using active machine learning.\n \n \n \n \n\n\n \n Zhong, M.; Tran, K.; Min, Y.; Wang, C.; Wang, Z.; Dinh, C.; De Luna, P.; Yu, Z.; Rasouli, A. S.; Brodersen, P.; Sun, S.; Voznyy, O.; Tan, C.; Askerka, M.; Che, F.; Liu, M.; Seifitokaldani, A.; Pang, Y.; Lo, S.; Ip, A.; Ulissi, Z.; and Sargent, E. H.\n\n\n \n\n\n\n Nature, 581(7807): 178–183. 5 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Accelerated$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 69 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{zhong2020accelerated,\n  author   = {Zhong, Miao and Tran, Kevin and Min, Yimeng and Wang, Chuanhao and Wang, Ziyun and Dinh, Cao-Thang and De Luna, Phil and Yu, Zongqian and Rasouli, Armin Sedighian and Brodersen, Peter and Sun, Song and Voznyy, Oleksandr and Tan, Chih-Shan and Askerka, Mikhail and Che, Fanglin and Liu, Min and Seifitokaldani, Ali and Pang, Yuanjie and Lo, Shen-Chuan and Ip, Alexander and Ulissi, Zachary and Sargent, Edward H.},\n  title    = {Accelerated discovery of CO2 electrocatalysts using active machine learning},\n  doi      = {10.1038/s41586-020-2242-8},\n  issn     = {1476-4687},\n  number   = {7807},\n  pages    = {178--183},\n  url      = {https://doi.org/10.1038/s41586-020-2242-8},\n  volume   = {581},\n  abstract = {The rapid increase in global energy demand and the need to replace carbon dioxide (CO2)-emitting fossil fuels with renewable sources have driven interest in chemical storage of intermittent solar and wind energy1,2. Particularly attractive is the electrochemical reduction of CO2 to chemical feedstocks, which uses both CO2 and renewable energy3-8. Copper has been the predominant electrocatalyst for this reaction when aiming for more valuable multi-carbon products9-16, and process improvements have been particularly notable when targeting ethylene. However, the energy efficiency and productivity (current density) achieved so far still fall below the values required to produce ethylene at cost-competitive prices. Here we describe Cu-Al electrocatalysts, identified using density functional theory calculations in combination with active machine learning, that efficiently reduce CO2 to ethylene with the highest Faradaic efficiency reported so far. This Faradaic efficiency of over 80 per cent (compared to about 66 per cent for pure Cu) is achieved at a current density of 400 milliamperes per square centimetre (at 1.5 volts versus a reversible hydrogen electrode) and a cathodic-side (half-cell) ethylene power conversion efficiency of 55 ± 2 per cent at 150 milliamperes per square centimetre. We perform computational studies that suggest that the Cu-Al alloys provide multiple sites and surface orientations with near-optimal CO binding for both efficient and selective CO2 reduction17. Furthermore, in situ X-ray absorption measurements reveal that Cu and Al enable a favourable Cu coordination environment that enhances C-C dimerization. These findings illustrate the value of computation and machine learning in guiding the experimental exploration of multi-metallic systems that go beyond the limitations of conventional single-metal electrocatalysts.},\n  journal  = {Nature},\n  refid    = {Zhong2020},\n  month = {5},\n  year     = {2020},\n}\n\n

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\n \n\n \n \n \n \n \n Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening.\n \n \n \n\n\n \n Gu, G. H.; Noh, J.; Kim, S.; Back, S.; Ulissi, Z.; and Jung, Y.\n\n\n \n\n\n\n The Journal of Physical Chemistry Letters, 11: 3185–3191. 3 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{gu2020practical,\n  title={Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening},\n  author={Gu, Geun Ho and Noh, Juhwan and Kim, Sungwon and Back, Seoin and Ulissi, Zachary and Jung, Yousung},\n  journal={The Journal of Physical Chemistry Letters},\n  volume={11},\n  pages={3185--3191},\n  month={3},\n  year={2020},\n  publisher={ACS Publications}\n}\n\n\n\n\n

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\n \n\n \n \n \n \n \n \n In silico discovery of active, stable, CO-tolerant and cost-effective electrocatalysts for hydrogen evolution and oxidation.\n \n \n \n \n\n\n \n Back, S.; Na, J.; Tran, K.; and Ulissi, Z. W.\n\n\n \n\n\n\n Phys. Chem. Chem. Phys., 22: 19454-19458. 8 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"In$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 13 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{back2020silico,\nauthor ="Back, Seoin and Na, Jonggeol and Tran, Kevin and Ulissi, Zachary W.",\ntitle  ="In silico discovery of active{,} stable{,} CO-tolerant and cost-effective electrocatalysts for hydrogen evolution and oxidation",\njournal  ="Phys. Chem. Chem. Phys.",\nyear  ="2020",\nvolume  ="22",\nissue  ="35",\npages  ="19454-19458",\nmonth={8},\npublisher  ="The Royal Society of Chemistry",\ndoi  ="10.1039/D0CP03017A",\nurl  ="http://dx.doi.org/10.1039/D0CP03017A",\nabstract  ="Various databases of density functional theory (DFT) calculations for materials and adsorption properties are currently available. Using the Materials Project and GASpy databases of material stability and binding energies (H* and CO*){,} respectively{,} we evaluate multiple aspects of catalysts to discover active{,} stable{,} CO-tolerant{,} and cost-effective hydrogen evolution and oxidation catalysts. Finally{,} we suggest a few candidate materials for future experimental validations. We highlight that the stability analysis is easily obtainable but provides invaluable information to assess thermodynamic and electrochemical stability{,} bridging the gap between simulations and experiments. Furthermore{,} it reduces the number of expensive DFT calculations required to predict catalytic activities of surfaces by filtering out unstable materials."}\n\n\n

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\n \n\n \n \n \n \n \n Discovery of Acid-Stable Oxygen Evolution Catalysts: High-throughput Computational Screening of Equimolar Bimetallic Oxides.\n \n \n \n\n\n \n Back, S.; Tran, K.; and Ulissi, Z. W\n\n\n \n\n\n\n ACS Applied Materials & Interfaces, 12(34): 38256–38265. 8 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{back2020discovery,\n  title={Discovery of Acid-Stable Oxygen Evolution Catalysts: High-throughput Computational Screening of Equimolar Bimetallic Oxides},\n  author={Back, Seoin and Tran, Kevin and Ulissi, Zachary W},\n  journal={ACS Applied Materials \\& Interfaces},\n  volume={12},\n  number={34},\n  pages={38256--38265},\n  year={2020},\n  publisher={ACS Publications},\n  month={8}\n}\n\n\n

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\n \n\n \n \n \n \n \n \n Enabling robust offline active learning for machine learning potentials using simple physics-based priors.\n \n \n \n \n\n\n \n Shuaibi, M.; Sivakumar, S.; Chen, R. Q.; and Ulissi, Z. W\n\n\n \n\n\n\n Machine Learning: Science and Technology. 12 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Enabling$ Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 9 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{10.1088/2632-2153/abcc44,\n\tauthor={Muhammed Shuaibi and Saurabh Sivakumar and Rui Qi Chen and Zachary W Ulissi},\n\ttitle={Enabling robust offline active learning for machine learning potentials using simple physics-based priors},\n\tjournal={Machine Learning: Science and Technology},\n\turl={http://iopscience.iop.org/article/10.1088/2632-2153/abcc44},\n\tyear={2020},\n\t  month={12},\n\tabstract={Machine learning surrogate models for quantum mechanical simulations has enabled the field to efficiently and accurately study material and molecular systems. Developed models typically rely on a substantial amount of data to make reliable predictions of the potential energy landscape or careful active learning and uncertainty estimates. When starting with small datasets, convergence of active learning approaches is a major outstanding challenge which limited most demonstrations to online active learning. In this work we demonstrate a Δ-machine learning approach that enables stable convergence in offline active learning strategies by avoiding unphysical configurations with initial datasets as little as a single data point. We demonstrate our framework's capabilities on a structural relaxation, transition state calculation, and molecular dynamics simulation, with the number of first principle calculations being cut down anywhere from 70-90\\%. The approach is incorporated and developed alongside AMP\\textit{torch}, an open-source machine learning potential package, along with interactive Google Colab notebook examples.}\n}\n\n\n

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\n \n\n \n \n \n \n \n Differentiable Optimization for the Prediction of Ground State Structures (DOGSS).\n \n \n \n\n\n \n Yoon, J.; and Ulissi, Z. W\n\n\n \n\n\n\n Physical Review Letters, 125(17): 173001. 2020.\n \n\n\n\n
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@article{yoon2020differentiable,\n  title={Differentiable Optimization for the Prediction of Ground State Structures (DOGSS)},\n  author={Yoon, Junwoong and Ulissi, Zachary W},\n  journal={Physical Review Letters},\n  volume={125},\n  number={17},\n  pages={173001},\n  year={2020},\n  publisher={APS}\n}\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage.\n \n \n \n\n\n \n Zitnick, C L.; Chanussot, L.; Das, A.; Goyal, S.; Heras-Domingo, J.; Ho, C.; Hu, W.; Lavril, T.; Palizhati, A.; Riviere, M.; and others\n\n\n \n\n\n\n arXiv preprint arXiv:2010.09435. 2020.\n \n\n\n\n
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@article{zitnick2020introduction,\n  title={An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage},\n  author={Zitnick, C Lawrence and Chanussot, Lowik and Das, Abhishek and Goyal, Siddharth and Heras-Domingo, Javier and Ho, Caleb and Hu, Weihua and Lavril, Thibaut and Palizhati, Aini and Riviere, Morgane and others},\n  journal={arXiv preprint arXiv:2010.09435},\n  year={2020}\n}\n\n\n

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\n \n 2019\n \n \n (4)\n \n \n

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\n \n\n \n \n \n \n \n \n Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts.\n \n \n \n \n\n\n \n Back, S.; Yoon, J.; Tian, N.; Zhong, W.; Tran, K.; and Ulissi, Z. W.\n\n\n \n\n\n\n The Journal of Physical Chemistry Letters, 10(15): 4401-4408. 7 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Convolutional$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 23 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{back2019convolutional,\nauthor = {Back, Seoin and Yoon, Junwoong and Tian, Nianhan and Zhong, Wen and Tran, Kevin and Ulissi, Zachary W.},\ntitle = {Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts},\njournal = {The Journal of Physical Chemistry Letters},\nvolume = {10},\nnumber = {15},\npages = {4401-4408},\nyear = {2019},\n  month={7},\ndoi = {10.1021/acs.jpclett.9b01428},\n\nURL = { \n        https://doi.org/10.1021/acs.jpclett.9b01428\n    \n},\neprint = { \n        https://doi.org/10.1021/acs.jpclett.9b01428\n    \n},\n\n\n}\n\n\n\n\n\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n \n Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning.\n \n \n \n \n\n\n \n Back, S.; Tran, K.; and Ulissi, Z. W.\n\n\n \n\n\n\n ACS Catalysis, 0(0): 7651-7659. 7 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Toward$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 11 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{back2019towards,\nauthor = {Back, Seoin and Tran, Kevin and Ulissi, Zachary W.},\ntitle = {Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning},\njournal = {ACS Catalysis},\nvolume = {0},\nnumber = {0},\npages = {7651-7659},\n  month={7},\nyear = {2019},\ndoi = {10.1021/acscatal.9b02416},\n\nURL = { \n        https://doi.org/10.1021/acscatal.9b02416\n    \n},\neprint = { \n        https://doi.org/10.1021/acscatal.9b02416\n    \n},\n\n}\n\n

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\n \n\n \n \n \n \n \n Towards Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks.\n \n \n \n\n\n \n Palizhati, A.; Zhong, W.; Tran, K.; Back, S.; and Ulissi, Z. W\n\n\n \n\n\n\n Journal of Chemical Information and Modeling. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{palizhati2019towards,\n  title={Towards Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks},\n  author={Palizhati, Aini and Zhong, Wen and Tran, Kevin and Back, Seoin and Ulissi, Zachary W},\n  journal={Journal of Chemical Information and Modeling},\n  year={2019},\n  doi={10.1021/acs.jcim.9b00550},\n  publisher={ACS Publications}\n}\n\n\n

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\n \n\n \n \n \n \n \n \n Optimization-Based Design of Active and Stable Nanostructured Surfaces.\n \n \n \n \n\n\n \n Hanselman, C. L.; Zhong, W.; Tran, K.; Ulissi, Z. W.; and Gounaris, C. E.\n\n\n \n\n\n\n The Journal of Physical Chemistry C, 123(48): 29209-29218. 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Optimization-Based$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{doi:10.1021/acs.jpcc.9b08431,\nauthor = {Hanselman, Christopher L. and Zhong, Wen and Tran, Kevin and Ulissi, Zachary W. and Gounaris, Chrysanthos E.},\ntitle = {Optimization-Based Design of Active and Stable Nanostructured Surfaces},\njournal = {The Journal of Physical Chemistry C},\nvolume = {123},\nnumber = {48},\npages = {29209-29218},\nyear = {2019},\ndoi = {10.1021/acs.jpcc.9b08431},\nURL = { \n        https://doi.org/10.1021/acs.jpcc.9b08431\n},\neprint = { \n        https://doi.org/10.1021/acs.jpcc.9b08431\n}\n\n}\n\n\n\n

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\n \n\n \n \n \n \n \n Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution.\n \n \n \n\n\n \n Tran, K.; and Ulissi, Z. W.\n\n\n \n\n\n\n Nature Catalysis, 1(9): 696. 9 2018.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 39 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{tran2018active,\n  title={Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution},\n  author={Tran, Kevin and Ulissi, Zachary W.},\n  journal={Nature Catalysis},\n  volume={1},\n  number={9},\n  pages={696},\n  month={9},\n  doi={10.1038/s41929-018-0142-1},\n  year={2018},\n  publisher={Nature Publishing Group},\n}\n\n

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\n \n\n \n \n \n \n \n \n Copper Silver Thin Films with Metastable Miscibility for Oxygen Reduction Electrocatalysis in Alkaline Electrolytes.\n \n \n \n \n\n\n \n Higgins, D.; Wette, M.; Gibbons, B. M.; Siahrostami, S.; Hahn, C.; Escudero-Escribano, M.; Garcia-Melchor, M.; Ulissi, Z. W.; Davis, R. C.; Mehta, A.; Clemens, B. M.; Nørskov, J. K.; and Jaramillo, T. F.\n\n\n \n\n\n\n ACS Applied Energy Materials. 5 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Copper$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{doi:10.1021/acsaem.8b00090,\n  author  = {Higgins, Drew and Wette, Melissa and Gibbons, Brenna M. and Siahrostami, Samira and Hahn, Christopher and Escudero-Escribano, Marıa and Garcia-Melchor, Max and Ulissi, Zachary W. and Davis, Ryan C. and Mehta, Apurva and Clemens, Bruce M. and N\\o{}rskov, Jens K. and Jaramillo, Thomas F.},\n  title   = {Copper Silver Thin Films with Metastable Miscibility for Oxygen Reduction Electrocatalysis in Alkaline Electrolytes},\n  journal = {ACS Applied Energy Materials},\n  year    = {2018},\n  month   = 5,\n  doi     = {10.1021/acsaem.8b00090},\n  eprint  = {https://doi.org/10.1021/acsaem.8b00090},\n  url     = { \n        https://doi.org/10.1021/acsaem.8b00090\n    \n},\n}\n\n\n\n\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n \n Theoretical Investigations of Transition Metal Surface Energies under Lattice Strain and CO Environment.\n \n \n \n \n\n\n \n Tang, M. T.; Ulissi, Z. W.; and Chan, K.\n\n\n \n\n\n\n The Journal of Physical Chemistry C, 122(26): 14481-14487. 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Theoretical$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{doi:10.1021/acs.jpcc.8b02094,\nauthor = {Tang, Michael\nT. and Ulissi, Zachary W. and Chan, Karen},\ntitle = {Theoretical Investigations of Transition Metal Surface Energies under Lattice Strain and CO Environment},\njournal = {The Journal of Physical Chemistry C},\nvolume = {122},\nnumber = {26},\npages = {14481-14487},\nyear = {2018},\ndoi = {10.1021/acs.jpcc.8b02094},\n\nURL = { \n        https://doi.org/10.1021/acs.jpcc.8b02094\n    \n},\neprint = { \n        https://doi.org/10.1021/acs.jpcc.8b02094\n    \n},\n\n}\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n Dynamic workflows for routine materials discovery in surface science.\n \n \n \n\n\n \n Tran, K.; Palizhati, A.; Back, S.; and Ulissi, Z. W\n\n\n \n\n\n\n Journal of Chemical Information and Modeling, 58(12): 2392–2400. 2018.\n \n\n\n\n
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@article{tran2018dynamic,\n  title={Dynamic workflows for routine materials discovery in surface science},\n  author={Tran, Kevin and Palizhati, Aini and Back, Seoin and Ulissi, Zachary W},\n  journal={Journal of Chemical Information and Modeling},\n  volume={58},\n  number={12},\n  pages={2392--2400},\n  year={2018},\n  doi = {10.1021/acs.jcim.8b00386},\n  publisher={ACS Publications}\n}\n\n\n\n\n

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\n \n\n \n \n \n \n \n To address surface reaction network complexity using scaling relations machine learning and DFT calculations.\n \n \n \n\n\n \n Ulissi, Z. W.; Medford, A. J.; Bligaard, T.; and Nørskov, J. K.\n\n\n \n\n\n\n Nature Communications, 8. March 2017.\n \n\n\n\n
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@Article{bib:networkreduction,\n  author    = {Ulissi, Zachary W. and Medford, A. J. and Bligaard, Thomas and N\\o{}rskov, Jens K.},\n  title     = {To address surface reaction network complexity using scaling relations machine learning and DFT calculations},\n  journal   = {Nature Communications},\n  year      = {2017},\n  volume    = {8},\n  month     = mar,\n  owner     = {zulissi},\n  doi = {10.1038/ncomms14621},\n  timestamp = {2017.01.05},\n}\n\n

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\n \n\n \n \n \n \n \n \n Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction.\n \n \n \n \n\n\n \n Ulissi, Z. W.; Tang, M. T.; Xiao, J.; Liu, X.; Torelli, D. A.; Karamad, M.; Cummins, K.; Hahn, C.; Lewis, N. S.; Jaramillo, T. F.; Chan, K.; and Nørskov, J. K.\n\n\n \n\n\n\n ACS Catalysis, 7(10): 6600-6608. October 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Machine-Learning$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{doi:10.1021/acscatal.7b01648,\n  author  = {Ulissi, Zachary W. and Tang, Michael T. and Xiao, Jianping and Liu, Xinyan and Torelli, Daniel A. and Karamad, Mohammadreza and Cummins, Kyle and Hahn, Christopher and Lewis, Nathan S. and Jaramillo, Thomas F. and Chan, Karen and N\\o{}rskov, Jens K.},\n  title   = {Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction},\n  journal = {ACS Catalysis},\n  year    = {2017},\n  volume  = {7},\n  number  = {10},\n  pages   = {6600-6608},\n  month   = oct,\n  doi     = {10.1021/acscatal.7b01648},\n  eprint  = {http://dx.doi.org/10.1021/acscatal.7b01648},\n  url     = { \n        http://dx.doi.org/10.1021/acscatal.7b01648\n    \n},\n}\n\n\n

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\n \n 2016\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n Persistently Auxetic Materials: Engineering the Poisson Ratio of 2D Self-Avoiding Membranes under Conditions of Non-Zero Anisotropic Strain.\n \n \n \n\n\n \n Ulissi, Z. W; Govind Rajan, A.; and Strano, M. S\n\n\n \n\n\n\n ACS Nano, 10(8): 7542–7549. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{ulissi2016persistently,\n  Title                    = {Persistently Auxetic Materials: Engineering the Poisson Ratio of 2D Self-Avoiding Membranes under Conditions of Non-Zero Anisotropic Strain},\n  Author                   = {Ulissi, Zachary W and Govind Rajan, Ananth and Strano, Michael S},\n  Journal                  = {ACS Nano},\n  Year                     = {2016},\n  Number                   = {8},\n  Pages                    = {7542--7549},\n  Volume                   = {10},\n  Doi                      = {10.1021/acsnano.6b02512},\n  Publisher                = {American Chemical Society}\n}\n\n

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\n \n\n \n \n \n \n \n Automated Discovery and Construction of Surface Phase Diagrams using Machine Learning.\n \n \n \n\n\n \n Ulissi, Z. W; Singh, A. R; Tsai, C.; and Nørskov, J. K.\n\n\n \n\n\n\n The Journal of Physical Chemistry Letters. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{ulissi2016automated,\n  author    = {Ulissi, Zachary W and Singh, Aayush R and Tsai, Charlie and N\\o{}rskov, Jens K.},\n  title     = {Automated Discovery and Construction of Surface Phase Diagrams using Machine Learning},\n  journal   = {The Journal of Physical Chemistry Letters},\n  year      = {2016},\n  abstract  = {Surface phase diagrams are necessary for understanding surface chemistry in electrochemical catalysis, where a range of adsorbates and coverages exist at varying applied potentials. These diagrams are typically constructed using intuition, which risks missing complex coverages and configurations at potentials of interest. More accurate cluster expansion methods are often difficult to implement quickly for new surfaces. We adopt a machine learning approach to rectify both issues. Using a Gaussian process regression model, the free energy of all possible adsorbate coverages for surfaces is predicted for a finite number of adsorption sites. Our result demonstrates a rational, simple, and systematic approach for generating accurate free-energy diagrams with reduced computational resources. The Pourbaix diagram for the IrO2(110) surface (with nine coverages from fully hydrogenated to fully oxygenated surfaces) is reconstructed using just 20 electronic structure relaxations, compared to approximately 90 using typical search methods. Similar efficiency is demonstrated for the MoS2 surface.},\n  doi       = {10.1021/acs.jpclett.6b01254},\n  publisher = {American Chemical Society},\n}\n\n

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\n \n 2015\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n A Mathematical Formulation and Solution of the CoPhMoRe Inverse Problem for Helically Wrapping Polymer Corona Phases on Cylindrical Substrates.\n \n \n \n\n\n \n Bisker, G.; Ahn, J.; Kruss, S.; Ulissi, Z. W; Salem, D. P; and Strano, M. S\n\n\n \n\n\n\n The Journal of Physical Chemistry C. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{bisker2015mathematical,\n  Title                    = {A Mathematical Formulation and Solution of the CoPhMoRe Inverse Problem for Helically Wrapping Polymer Corona Phases on Cylindrical Substrates},\n  Author                   = {Bisker, Gili and Ahn, Jiyoung and Kruss, Sebastian and Ulissi, Zachary W and Salem, Daniel P and Strano, Michael S},\n  Journal                  = {The Journal of Physical Chemistry C},\n  Year                     = {2015},\n\n  Abstract                 = {Corona phase molecular recognition (CoPhMoRe) is a new technique that generates a nanoparticle-coupled polymer phase, capable of recognizing a specific molecule with high affinity and selectivity. CoPhMoRe has been successfully demonstrated using polymer wrapped single walled carbon nanotubes, resulting in molecular recognition complexes, to date, for dopamine, estradiol, riboflavin, and l-thyroxine, utilizing combinatorial library screening. A rational alternative design to this empirical library screening is to solve the mathematical formulation that we introduce as the CoPhMoRe inverse problem. This inverse problem seeks a linear function representing the position of monomers or functional groups along a polymer backbone that results in a 3-dimensional structure capable of recognizing a specific molecule when mapped to a nanoparticle surface. The potential solution space for such an inverse problem is infinite in general, but for the specific constraint of a helically wrapping polymer, mapped to a cylindrical nanoparticle, we show in this work that two types of inverse problems are exactly solvable. In one case, the polymer pitch and composition can be designed to allow for the specific binding of a small molecule analyte in the occluded space on the nanotube surface. In the other, a larger macromolecule can interact with a deformed helix, which partially conforms to it. A simplified, coarse-grained molecular model of a helically wrapping polymer demonstrates the inhomogeneous binding potential formed by a wrapping with a given pitch. Calculating the potential maps for various pitch values illustrates that there is an optimal pitch that enables the selective and specific binding of the target analyte. An additional coarse-grained model of a helical wrapping by a polymer consisting of alternating hydrophobic–hydrophilic segments demonstrates the resulting deformed helix corona around the nanotube, which forms accessible binding pockets between the hydrophilic loops. While these are the idealized forms of actual CoPhMoRe phases, the formation and solution of such inverse problems 5 serve to reduce the dimensionality of library screening for CoPhMoRe discoveries, as well as provide a theoretical basis for understanding certain types of CoPhMoRe recognition.},\n  Doi                      = {10.1021/acs.jpcc.5b01705},\n  Owner                    = {zulissi},\n  Publisher                = {American Chemical Society},\n  Timestamp                = {2015.05.19}\n}\n\n\n\n\n\n\n

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\n\n\n

\n Corona phase molecular recognition (CoPhMoRe) is a new technique that generates a nanoparticle-coupled polymer phase, capable of recognizing a specific molecule with high affinity and selectivity. CoPhMoRe has been successfully demonstrated using polymer wrapped single walled carbon nanotubes, resulting in molecular recognition complexes, to date, for dopamine, estradiol, riboflavin, and l-thyroxine, utilizing combinatorial library screening. A rational alternative design to this empirical library screening is to solve the mathematical formulation that we introduce as the CoPhMoRe inverse problem. This inverse problem seeks a linear function representing the position of monomers or functional groups along a polymer backbone that results in a 3-dimensional structure capable of recognizing a specific molecule when mapped to a nanoparticle surface. The potential solution space for such an inverse problem is infinite in general, but for the specific constraint of a helically wrapping polymer, mapped to a cylindrical nanoparticle, we show in this work that two types of inverse problems are exactly solvable. In one case, the polymer pitch and composition can be designed to allow for the specific binding of a small molecule analyte in the occluded space on the nanotube surface. In the other, a larger macromolecule can interact with a deformed helix, which partially conforms to it. A simplified, coarse-grained molecular model of a helically wrapping polymer demonstrates the inhomogeneous binding potential formed by a wrapping with a given pitch. Calculating the potential maps for various pitch values illustrates that there is an optimal pitch that enables the selective and specific binding of the target analyte. An additional coarse-grained model of a helical wrapping by a polymer consisting of alternating hydrophobic–hydrophilic segments demonstrates the resulting deformed helix corona around the nanotube, which forms accessible binding pockets between the hydrophilic loops. While these are the idealized forms of actual CoPhMoRe phases, the formation and solution of such inverse problems 5 serve to reduce the dimensionality of library screening for CoPhMoRe discoveries, as well as provide a theoretical basis for understanding certain types of CoPhMoRe recognition.\n

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\n \n\n \n \n \n \n \n A 2D Equation-of-State Model for Corona Phase Molecular Recognition on Single-Walled Carbon Nanotube and Graphene Surfaces.\n \n \n \n\n\n \n Ulissi, Z. W.; Zhang, J.; Sresht, V.; Blankschtein, D.; and Strano, M. S.\n\n\n \n\n\n\n Langmuir, 31(1): 628–636. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{bib:2deos,\n  Title                    = {A 2D Equation-of-State Model for Corona Phase Molecular Recognition on Single-Walled Carbon Nanotube and Graphene Surfaces},\n  Author                   = {Ulissi, Zachary W. and Zhang, Jingqing and Sresht, Vishnu and Blankschtein, Daniel and Strano, Michael S.},\n  Journal                  = {Langmuir},\n  Year                     = {2015},\n  Number                   = {1},\n  Pages                    = {628–636},\n  Volume                   = {31},\n\n\n  Doi                      = {10.1021/la503899e},\n  Owner                    = {zulissi},\n  Timestamp                = {2014.12.03}\n}\n

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\n \n 2014\n \n \n (4)\n \n \n

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\n \n\n \n \n \n \n \n \n Deterministic modelling of carbon nanotube near-infrared solar cells.\n \n \n \n \n\n\n \n Bellisario, D. O.; Jain, R. M.; Ulissi, Z. W.; and Strano, M. S.\n\n\n \n\n\n\n Energy Environ. Sci., 7: 3769-3781. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Deterministic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{C4EE01765J,\n  Title                    = {Deterministic modelling of carbon nanotube near-infrared solar cells},\n  Author                   = {Bellisario, Darin O. and Jain, Rishabh M. and Ulissi, Zachary W. and Strano, Michael S.},\n  Journal                  = {Energy Environ. Sci.},\n  Year                     = {2014},\n  Pages                    = {3769-3781},\n  Volume                   = {7},\n  Doi                      = {10.1039/C4EE01765J},\n  Issue                    = {11},\n  Owner                    = {zulissi},\n  Publisher                = {The Royal Society of Chemistry},\n  Timestamp                = {2014.12.11},\n  Url                      = {http://dx.doi.org/10.1039/C4EE01765J}\n}\n\n

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\n \n\n \n \n \n \n \n \n Quantitative Theory of Adsorptive Separation for the Electronic Sorting of Single-Walled Carbon Nanotubes.\n \n \n \n \n\n\n \n Jain, R. M.; Tvrdy, K.; Han, R.; Ulissi, Z. W.; and Strano, M. S.\n\n\n \n\n\n\n ACS Nano, 8(4): 3367-3379. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantitative$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{doi:10.1021/nn4058402,\n  Title                    = {Quantitative Theory of Adsorptive Separation for the Electronic Sorting of Single-Walled Carbon Nanotubes},\n  Author                   = {Jain, Rishabh M. and Tvrdy, Kevin and Han, Rebecca and Ulissi, Zachary W. and Strano, Michael S.},\n  Journal                  = {ACS Nano},\n  Year                     = {2014},\n  Number                   = {4},\n  Pages                    = {3367-3379},\n  Volume                   = {8},\n\n  Abstract                 = {Recently, several important advances in techniques for the separation of single-walled carbon nanotubes (SWNTs) by chiral index have been developed. These new methods allow for the separation of SWNTs through selective adsorption and desorption of different (n,m) chiral indices to and from a specific hydrogel. Our group has previously developed a kinetic model for the chiral elution order of separation; however, the underlying mechanism that allows for this separation remains unknown. In this work, we develop a quantitative theory that provides the first mechanistic insights for the separation order and binding kinetics of each SWNT chirality (n,m) based on the surfactant-induced, linear charge density, which we find ranges from 0.41 e–/nm for (7,3) SWNTs in 17 mM sodium dodecyl sulfate (SDS) to 3.32 e–/nm for (6,5) SWNTs in 105 mM SDS. Adsorption onto the hydrogel support is balanced by short-distance hard-surface and long-distance electrostatic repulsive SWNT/substrate forces, the latter of which we postulate is strongly dependent on surfactant concentration and ultimately leads to gel-based single-chirality semiconducting SWNT separation. These molecular-scale properties are derived using bulk-phase, forward adsorption rate constants for each SWNT chirality in accordance with our previously published model. The theory developed here quantitatively describes the experimental elution profiles of 15 unique SWNT chiralities as a function of anionic surfactant concentration between 17 and 105 mM, as well as phenomenological observations of the impact of varying preparatory conditions such as extent of ultrasonication and ultracentrifugation. We find that SWNT elution order and separation efficiency are primarily driven by the morphological change of SDS surfactant wrapping on the surface of the nanotube, mediated by SWNT chirality and the ionic strength of the surrounding medium. This work provides a foundational understanding for high-purity, preparative-scale separation of as-produced SWNT mixtures into isolated, single-chirality fractions.},\n  Doi                      = {10.1021/nn4058402},\n  Eprint                   = {http://pubs.acs.org/doi/pdf/10.1021/nn4058402},\n  Owner                    = {zulissi},\n  Timestamp                = {2014.07.12},\n  Url                      = {http://pubs.acs.org/doi/abs/10.1021/nn4058402}\n}\n\n

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\n\n\n

\n Recently, several important advances in techniques for the separation of single-walled carbon nanotubes (SWNTs) by chiral index have been developed. These new methods allow for the separation of SWNTs through selective adsorption and desorption of different (n,m) chiral indices to and from a specific hydrogel. Our group has previously developed a kinetic model for the chiral elution order of separation; however, the underlying mechanism that allows for this separation remains unknown. In this work, we develop a quantitative theory that provides the first mechanistic insights for the separation order and binding kinetics of each SWNT chirality (n,m) based on the surfactant-induced, linear charge density, which we find ranges from 0.41 e–/nm for (7,3) SWNTs in 17 mM sodium dodecyl sulfate (SDS) to 3.32 e–/nm for (6,5) SWNTs in 105 mM SDS. Adsorption onto the hydrogel support is balanced by short-distance hard-surface and long-distance electrostatic repulsive SWNT/substrate forces, the latter of which we postulate is strongly dependent on surfactant concentration and ultimately leads to gel-based single-chirality semiconducting SWNT separation. These molecular-scale properties are derived using bulk-phase, forward adsorption rate constants for each SWNT chirality in accordance with our previously published model. The theory developed here quantitatively describes the experimental elution profiles of 15 unique SWNT chiralities as a function of anionic surfactant concentration between 17 and 105 mM, as well as phenomenological observations of the impact of varying preparatory conditions such as extent of ultrasonication and ultracentrifugation. We find that SWNT elution order and separation efficiency are primarily driven by the morphological change of SDS surfactant wrapping on the surface of the nanotube, mediated by SWNT chirality and the ionic strength of the surrounding medium. This work provides a foundational understanding for high-purity, preparative-scale separation of as-produced SWNT mixtures into isolated, single-chirality fractions.\n

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\n \n\n \n \n \n \n \n \n Spatiotemporal Intracellular Nitric Oxide Signaling Captured using Internalized, Near Infrared Fluorescent Carbon Nanotube Nanosensors.\n \n \n \n \n\n\n \n Ulissi, Z. W.; Sen, F.; Gong, X.; Sen, S.; Iverson, N.; Boghossian, A. A.; Godoy, L.; Wogan, G.; Mukhopadhyay, D.; and Strano, M. S.\n\n\n \n\n\n\n Nano Letters, 14: 4887-4894. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Spatiotemporal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{bib:jsk,\n  Title                    = {Spatiotemporal Intracellular Nitric Oxide Signaling Captured using Internalized, Near Infrared Fluorescent Carbon Nanotube Nanosensors},\n  Author                   = {Ulissi, Zachary W. and Sen, Fatih and Gong, Xun and Sen, Selda and Iverson, Nicole and Boghossian, Ardemis A. and Godoy, Luiz and Wogan, Gerald and Mukhopadhyay, D. and Strano, Michael S.},\n  Journal                  = {Nano Letters},\n  Year                     = {2014},\n  Pages                    = {4887-4894},\n  Volume                   = {14},\n\n  Abstract                 = {Fluorescent nanosensor probes have suffered from limited molecular recognition and a dearth of strategies for spatial-temporal operation in cell culture. In this work, we spatially imaged the dynamics of nitric oxide (NO) signaling, important in numerous pathologies and physiological functions, using intracellular near-infrared fluorescent single-walled carbon nanotubes. The observed spatial-temporal NO signaling gradients clarify and refine the existing paradigm of NO signaling based on averaged local concentrations. This work enables the study of transient intracellular phenomena associated with signaling and therapeutics.},\n  Doi                      = {10.1021/nl502338y},\n  Owner                    = {zulissi},\n  Timestamp                = {2014.07.12},\n  Url                      = {http://pubs.acs.org/doi/abs/10.1021/nl502338y}\n}\n\n

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\n \n\n \n \n \n \n \n Low Dimensional Carbon Materials for Applications in Mass and Energy Transport.\n \n \n \n\n\n \n Wang, Q. H.; Bellisario, D. O.; Drahushuk, L. W.; Jain, R. M.; Kruss, S.; Landry, M. P.; Mahajan, S. G.; Shimizu, S. F. E.; Ulissi, Z. W.; and Strano, M. S.\n\n\n \n\n\n\n Chemistry of Materials, 26(1): 172-183. 1 2014.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{RefWorks:1037,\n  Title                    = {Low Dimensional Carbon Materials for Applications in Mass and Energy Transport},\n  Author                   = {Qing Hua Wang and Darin O. Bellisario and Lee W. Drahushuk and Rishabh M. Jain and Sebastian Kruss and Markita P. Landry and Sayalee G. Mahajan and Steven F. E. Shimizu and Zachary W. Ulissi and Michael S. Strano},\n  Journal                  = {Chemistry of Materials},\n  Year                     = {2014},\n\n  Month                    = {1},\n  Number                   = {1},\n  Pages                    = {172-183},\n  Volume                   = {26},\n\n  Abstract                 = {Low dimensional materials are those that possess at least one physical boundary small enough to confine the electrons or phonons. This quantum confinement reduces the dimensionality of the material and imparts unique and novel properties that are not seen in their bulk forms. Examples include quantum dots (0-D), carbon nanotubes (1-D), and graphene (2-D). Accordingly, these materials exhibit new concepts in mass and energy transport that can be exploited for technological applications. In this Perspective, we review several topics related to mass and energy transport in and around carbon-based low dimensional materials. Recent developments in the study of matter being transported through carbon nanotube and graphene nanopores are reviewed, as well as applications of excitonic, thermal, and electronic energy transport in carbon nanotubes. The nanometer-scale interior of a single-walled carbon nanotube (SWCNT) has been studied as a unique nanopore, exhibiting periodic ionic conduction currents and dimensionally confined material phases. The mechanism of gas transport through atomic-scale holes in graphene, which is otherwise a perfect barrier material, has been analytically studied. These insights on nanoscale mass transport will have important implications in systems ranging from biological nanopores to advanced water filtration devices. The electronic structure of semiconducting SWCNTs allows photogenerated excitons to be harnessed for single-molecule biosensing and as elements of a new class of all-nanocarbon near-infrared photovoltaics. The extremely high thermal and electrical conductivities of carbon nanotubes allows the generation of electrical energy from chemical reactions. The understanding of how low dimensional physics and chemistry influences mass and energy transport will facilitate the application of these materials to a variety of scientific challenges.},\n  Doi                      = {10.1021/cm402895e},\n%   ISBN                     = {0897-4756; 1520-5002}\n}\n\n

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\n \n 2013\n \n \n (6)\n \n \n

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\n \n\n \n \n \n \n \n A Quantitative and Predictive Model of Electromigration-Induced Breakdown of Metal Nanowires.\n \n \n \n\n\n \n Bellisario, D. O.; Ulissi, Z. W.; and Strano, M. S.\n\n\n \n\n\n\n Journal of Physical Chemistry C, 117(23): 12373–12378. 6 2013.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{ISI:000320640500056,\n  Title                    = {A Quantitative and Predictive Model of Electromigration-Induced Breakdown of Metal Nanowires},\n  Author                   = {Bellisario, Darin O. and Ulissi, Zachary W. and Strano, Michael S.},\n  Journal                  = {Journal of Physical Chemistry C},\n  Year                     = {2013},\n\n  Month                    = {6},\n  Number                   = {23},\n  Pages                    = {12373--12378},\n  Volume                   = {117},\n\n  Abstract                 = {An isothermal model of electromigration breakdown of metal nanowires 80-700 nm in diameter is developed and validated using experimental data obtained from isolated cylindrical Au nanowires. The model considers electromigration from an applied current producing a net flux of metal atoms, reducing the nanowire radius and conductivity precipitously and accounting for both mass and electronic carrier transport. The model successfully predicts the observed critical failure current, the correct scaling with nanowire radius to 3/2 power, and the impedance evolution prior to breakdown. Application to the case where feedback control is employed to limit the rate of nanowire thinning reproduces key features, including slowed necking, a threshold current and voltage after which lower bias is required to advance formation, and the dependence of these values on feedback parameters.},\n  Doi                      = {10.1021/jp40357761},\n  ISSN                     = {1932-7447},\n  Unique-id                = {ISI:000320640500056}\n}\n\n

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\n \n\n \n \n \n \n \n Charge Transfer at Junctions of a Single Layer of Graphene and a Metallic Single Walled Carbon Nanotube.\n \n \n \n\n\n \n Paulus, G. L. C.; Wang, Q. H.; Ulissi, Z. W.; McNicholas, T. P.; Vijayaraghavan, A.; Shih, C.; Jin, Z.; and Strano, M. S.\n\n\n \n\n\n\n Small, 9(11): 1954–1963. 6 2013.\n \n\n\n\n
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@Article{ISI:000319833700012,\n  Title                    = {Charge Transfer at Junctions of a Single Layer of Graphene and a Metallic Single Walled Carbon Nanotube},\n  Author                   = {Paulus, Geraldine L. C. and Wang, Qing Hua and Ulissi, Zachary W. and McNicholas, Thomas P. and Vijayaraghavan, Aravind and Shih, Chih-Jen and Jin, Zhong and Strano, Michael S.},\n  Journal                  = {Small},\n  Year                     = {2013},\n\n  Month                    = {6},\n  Number                   = {11},\n  Pages                    = {1954--1963},\n  Volume                   = {9},\n\n  Abstract                 = {Junctions between a single walled carbon nanotube (SWNT) and a monolayer of graphene are fabricated and studied for the first time. A single layer graphene (SLG) sheet grown by chemical vapor deposition (CVD) is transferred onto a SiO2/Si wafer with aligned CVD-grown SWNTs. Raman spectroscopy is used to identify metallic-SWNT/SLG junctions, and a method for spectroscopic deconvolution of the overlapping G peaks of the SWNT and the SLG is reported, making use of the polarization dependence of the SWNT. A comparison of the Raman peak positions and intensities of the individual SWNT and graphene to those of the SWNT-graphene junction indicates an electron transfer of 1.12 x 1013 cm-2 from the SWNT to the graphene. This direction of charge transfer is in agreement with the work functions of the SWNT and graphene. The compression of the SWNT by the graphene increases the broadening of the radial breathing mode (RBM) peak from 3.6 +/- 0.3 to 4.6 +/- 0.5 cm-1 and of the G peak from 13 +/- 1 to 18 +/- 1 cm-1, in reasonable agreement with molecular dynamics simulations. However, the RBM and G peak position shifts are primarily due to charge transfer with minimal contributions from strain. With this method, the ability to dope graphene with nanometer resolution is demonstrated.},\n  Doi                      = {10.1002/smll.201201034},\n  ISSN                     = {1613-6810},\n  Orcid-numbers            = {Vijayaraghavan, Aravind/0000-0001-8289-2337 },\n  Researcherid-numbers     = {Vijayaraghavan, Aravind/E-1087-2011 Jin, Zhong/D-1742-2012 Shih, Chih-Jen/B-1185-2013},\n  Unique-id                = {ISI:000319833700012}\n}\n\n

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\n \n\n \n \n \n \n \n Stochastic Pore Blocking and Gating in PDMS-Glass Nanopores from Vapor-Liquid Phase Transitions.\n \n \n \n\n\n \n Shimizu, S.; Ellison, M.; Aziz, K.; Wang, Q. H.; Ulissi, Z. W.; Gunther, Z.; Bellisario, D.; and Strano, M.\n\n\n \n\n\n\n Journal of Physical Chemistry C, 117(19): 9641–9651. 5 2013.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{ISI:000319649100015,\n  Title                    = {Stochastic Pore Blocking and Gating in PDMS-Glass Nanopores from Vapor-Liquid Phase Transitions},\n  Author                   = {Shimizu, Steven and Ellison, Mark and Aziz, Kimberly and Wang, Qing Hua and Ulissi, Zachary W. and Gunther, Zachary and Bellisario, Darin and Strano, Michael},\n  Journal                  = {Journal of Physical Chemistry C},\n  Year                     = {2013},\n\n  Month                    = {5},\n  Number                   = {19},\n  Pages                    = {9641--9651},\n  Volume                   = {117},\n\n  Abstract                 = {Polydimethylsiloxane (PDMS) is commonly used in research for microfluidic devices and for making elastomeric stamps for soft lithography. Its biocompatibility and nontoxicitiy also allow it to be used in personal care, food, and medical products. Herein we report a phenomenon observed when patch clamp, a technique normally used to study biological ion channels, is performed on both grooved and planar PDMS surfaces, resulting in stochastic current fluctuations that are due to a nanopore being formed at the interface of the PDMS and glass surfaces and being randomly blocked. Deformable pores between 1.9 +/- 0.7 and 7.4 +/- 2.1 nm in diameter, depending on the calculation method, form upon patching to the surface. Coulter blocking and nanoprecipitation are ruled out, and we instead propose a mechanism of stochastic current fluctuations arising from transitions between vapor and liquid phases, consistent with similar observations and theory from statistical mechanics literature. Interestingly, we find that {[}Ru(bpy)(3)](2+), a common probe molecule employed in nanopore research, physisorbs inside these hydrophobic nanopores blocking all ionic current flow at concentrations higher than 1 X 10(-4) M, despite the considerably larger pore diameter relative to the molecule. Patch clamp methods are promising for the study of stochastic current fluctuations and other transport phenomenon in synthetic nanopore systems.},\n  Doi                      = {10.1021/jp312659m},\n  ISSN                     = {1932-7447},\n  Unique-id                = {ISI:000319649100015}\n}\n\n

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\n \n\n \n \n \n \n \n Control of nano and microchemical systems.\n \n \n \n\n\n \n Ulissi, Z. W.; Strano, M. S.; and Braatz, R. D.\n\n\n \n\n\n\n Computers & Chemical Engineering, 51(SI): 149-156. 4 2013.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{ISI:000314993000014,\n  Title                    = {Control of nano and microchemical systems},\n  Author                   = {Ulissi, Zachary W. and Strano, Michael S. and Braatz, Richard D.},\n  Journal                  = {Computers \\& Chemical Engineering},\n  Year                     = {2013},\n\n  Month                    = {4},\n  Number                   = {SI},\n  Pages                    = {149-156},\n  Volume                   = {51},\n\n  Abstract                 = {Many advances in the development of nano and microchemical systems have occurred in the last decade. These systems have significant associated identification and control challenges, including high state dimensionality, limitations in real-time measurements and manipulated variables, and significant uncertainties described by non-Gaussian distributions. Some strategies for addressing these challenges are summarized, which include exploiting structure within the stochastic Master equations that describe molecular interactions, manipulating molecular bonds at system boundaries, and manipulating molecules and nanoscale objects through magnetic and electric fields. The strategies are illustrated in a variety of applications that include the estimation of nucleation kinetics of protein and pharmaceutical crystals within fluidic devices, the estimation of concentration fields using DNA-wrapped single-walled carbon nanotube-based sensor arrays, the simultaneous control of nanoscale geometry and electrical activation during thermal annealing in a semiconductor material, and the control of nanostructure formation on surfaces. Promising directions for research and technology development are identified for the next decade. (C) 2012 Elsevier Ltd. All rights reserved.},\n  Doi                      = {10.1016/j.compchemeng.2012.07.004},\n  ISSN                     = {0098-1354},\n  Unique-id                = {ISI:000314993000014}\n}\n\n

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\n \n\n \n \n \n \n \n Diameter-dependent ion transport through the interior of isolated single-walled carbon nanotubes.\n \n \n \n\n\n \n Choi, W.; Ulissi, Z. W; Shimizu, S. F.; Bellisario, D. O; Ellison, M. D; and Strano, M. S\n\n\n \n\n\n\n Nature Communications, 4: 2397. 2013.\n \n\n\n\n
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@article{choi2013diameter,\n  title={Diameter-dependent ion transport through the interior of isolated single-walled carbon nanotubes},\n  author={Choi, Wonjoon and Ulissi, Zachary W and Shimizu, Steven FE and Bellisario, Darin O and Ellison, Mark D and Strano, Michael S},\n  journal={Nature Communications},\n  volume={4},\n  pages={2397},\n  year={2013},\n  doi={10.1038/ncomms3397},\n  publisher={Nature Publishing Group}\n}\n\n

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\n \n\n \n \n \n \n \n Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes.\n \n \n \n\n\n \n Zhang, J.; Landry, M. P.; Barone, P. W.; Kim, J.; Lin, S.; Ulissi, Z. W.; Lin, D.; Mu, B.; Boghossian, A. A.; Hilmer, A. J.; Rwei, A.; Hinckley, A. C.; Kruss, S.; Shandell, M. A.; Nair, N.; Blake, S.; Sen, F.; Sen, S.; Croy, R. G.; Li, D.; Yum, K.; Ahn, J.; Jin, H.; Heller, D. A.; Essigmann, J. M.; Blankschtein, D.; and Strano, M. S.\n\n\n \n\n\n\n Nature Nanotechnology, 8(12): 959–968. 12 2013.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{ISI:000327943400026,\n  Title                    = {Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes},\n  Author                   = {Zhang, Jingqing and Landry, Markita P. and Barone, Paul W. and Kim, Jong-Ho and Lin, Shangchao and Ulissi, Zachary W. and Lin, Dahua and Mu, Bin and Boghossian, Ardemis A. and Hilmer, Andrew J. and Rwei, Alina and Hinckley, Allison C. and Kruss, Sebastian and Shandell, Mia A. and Nair, Nitish and Blake, Steven and Sen, Fatih and Sen, Selda and Croy, Robert G. and Li, Deyu and Yum, Kyungsuk and Ahn, Jin-Ho and Jin, Hong and Heller, Daniel A. and Essigmann, John M. and Blankschtein, Daniel and Strano, Michael S.},\n  Journal                  = {Nature Nanotechnology},\n  Year                     = {2013},\n\n  Month                    = {12},\n  Number                   = {12},\n  Pages                    = {959--968},\n  Volume                   = {8},\n\n  Abstract                 = {Understanding molecular recognition is of fundamental importance in applications such as therapeutics, chemical catalysis and sensor design. The most common recognition motifs involve biological macromolecules such as antibodies and aptamers. The key to biorecognition consists of a unique three-dimensional structure formed by a folded and constrained bioheteropolymer that creates a binding pocket, or an interface, able to recognize a specific molecule. Here, we show that synthetic heteropolymers, once constrained onto a single-walled carbon nanotube by chemical adsorption, also form a new corona phase that exhibits highly selective recognition for specific molecules. To prove the generality of this phenomenon, we report three examples of heteropolymer-nanotube recognition complexes for riboflavin, L-thyroxine and oestradiol. In each case, the recognition was predicted using a two-dimensional thermodynamic model of surface interactions in which the dissociation constants can be tuned by perturbing the chemical structure of the heteropolymer. Moreover, these complexes can be used as new types of spatiotemporal sensors based on modulation of the carbon nanotube photoemission in the near-infrared, as we show by tracking riboflavin diffusion in murine macrophages.},\n  Doi                      = {10.1038/NNANO.2013.236},\n  ISSN                     = {1748-3387},\n  Unique-id                = {ISI:000327943400026}\n}\n\n

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\n \n\n \n \n \n \n \n Modelling and development of photoelectrochemical reactor for H-2 production.\n \n \n \n\n\n \n Carver, C.; Ulissi, Z. W.; Ong, C. K.; Dennison, S.; Kelsall, G. H.; and Hellgardt, K.\n\n\n \n\n\n\n International Journal of Hydrogen Energy, 37(3): 2911–2923. 2 2012.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{ISI:000301157300094,\n  Title                    = {Modelling and development of photoelectrochemical reactor for H-2 production},\n  Author                   = {Carver, C. and Ulissi, Zachary W. and Ong, C. K. and Dennison, S. and Kelsall, G. H. and Hellgardt, K.},\n  Journal                  = {International Journal of Hydrogen Energy},\n  Year                     = {2012},\n\n  Month                    = {2},\n  Number                   = {3},\n  Pages                    = {2911--2923},\n  Volume                   = {37},\n\n  Abstract                 = {Photoelectrolysis of aqueous solutions, using one or more semiconducting electrodes in a photoelectrochemical reactor, is a potentially attractive process for hydrogen production because of its prospectively high energy efficiency, simplicity and potentially low cost. The design requirements and preliminary results of modelling a photoelectrochemical (PEC) reactor are described. Potential and current density distributions, due to ohmic potential losses in thin (non-photo) anodes on poorly conducting fluoride-doped tin oxide coated glass substrates, were modelled. The predicted current densities decayed rapidly from the terminals at the edges, towards the centre of a 0.1 x 0.1 m(2) anode, so limiting scale-up with such substrates. Spatial distributions of dissolved oxygen concentrations were also modelled, aiming to define operating conditions that would avoid forming bubbles, which reflect light specularly decreasing photon absorption efficiencies of photoelectrodes. The implications for the future optimization of the reactor are discussed. Copyright (C) 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.},\n  Doi                      = {10.1016/j.ijhydene.2011.07.012},\n  ISSN                     = {0360-3199},\n  Organization             = {AIChE},\n  Unique-id                = {ISI:000301157300094}\n}\n\n

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\n \n\n \n \n \n \n \n Observation of Oscillatory Surface Reactions of Riboflavin, Trolox, and Singlet Oxygen Using Single Carbon Nanotube Fluorescence Spectroscopy.\n \n \n \n\n\n \n Sen, F.; Boghossian, A. A.; Sen, S.; Ulissi, Z. W.; Zhang, J.; and Strano, M. S.\n\n\n \n\n\n\n ACS Nano, 6(12): 10632–10645. 12 2012.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{ISI:000312563600024,\n  Title                    = {Observation of Oscillatory Surface Reactions of Riboflavin, Trolox, and Singlet Oxygen Using Single Carbon Nanotube Fluorescence Spectroscopy},\n  Author                   = {Sen, Fatih and Boghossian, Ardemis A. and Sen, Selda and Ulissi, Zachary W. and Zhang, Jingqing and Strano, Michael S.},\n  Journal                  = {ACS Nano},\n  Year                     = {2012},\n\n  Month                    = {12},\n  Number                   = {12},\n  Pages                    = {10632--10645},\n  Volume                   = {6},\n\n  Abstract                 = {Single-molecule fluorescent microscopy allows semiconducting single-walled carbon nanotubes (SWCNTs) to detect the adsorption and desorption of single adsorbate molecules as a stochastic modulation of emission intensity. In this study, we identify and assign the signature of the complex decomposition and reaction pathways of riboflavin in the presence of the free radical scavenger Trolox using DNA-wrapped SWCNT sensors dispersed onto an aminopropyltriethoxysilane (APTES) coated surface. SWCNT emission is quenched by riboflavin-induced reactive oxygen species (ROS), but increases upon the adsorption of Trolox, which functions as a reductive brightening agent. Riboflavin has two parallel reaction pathways, a Trolox oxidizer and a photosensitizer for singlet oxygen and superoxide generation. The resulting reaction network can be detected in real time in the vicinity of a single SWCNT and can be completely described using elementary reactions and kinetic rate constants measured independently. The reaction mechanism results in an oscillatory fluorescence response from each SWCNT, allowing for the simultaneous detection of multiple reactants. A series-parallel kinetic model is shown to describe the critical points of these oscillations, with partition coefficients on the order of 10(6) - 10(4) for the reactive oxygen and excited state species. These results highlight the potential for SWCNTs to characterize complex reaction networks at the nanometer scale.},\n  Doi                      = {10.1021/nn303716n},\n  ISSN                     = {1936-0851},\n  Unique-id                = {ISI:000312563600024}\n}\n\n

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\n \n 2011\n \n \n (4)\n \n \n

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\n \n\n \n \n \n \n \n \n The chemical dynamics of nanosensors capable of single-molecule detection.\n \n \n \n \n\n\n \n Boghossian, A. A.; Zhang, J.; Le Floch-Yin, F. T.; Ulissi, Z. W.; Bojo, P.; Han, J.; Kim, J.; Arkalgud, J. R.; Reuel, N. F.; Braatz, R. D.; and Strano, M. S.\n\n\n \n\n\n\n The Journal of Chemical Physics, 135(8): 084124. 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{doi:10.1063/1.3606496,\nauthor = {Boghossian,Ardemis A.  and Zhang,Jingqing  and Le Floch-Yin,François T.  and Ulissi,Zachary W.  and Bojo,Peter  and Han,Jae-Hee  and Kim,Jong-Ho  and Arkalgud,Jyoti R.  and Reuel,Nigel F.  and Braatz,Richard D.  and Strano,Michael S. },\ntitle = {The chemical dynamics of nanosensors capable of single-molecule detection},\njournal = {The Journal of Chemical Physics},\nvolume = {135},\nnumber = {8},\npages = {084124},\nyear = {2011},\ndoi = {10.1063/1.3606496},\n\nURL = { \n        https://doi.org/10.1063/1.3606496\n    \n},\neprint = { \n        https://doi.org/10.1063/1.3606496\n    \n}\n\n}\n\n\n\n\n

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\n \n\n \n \n \n \n \n Effect of multiscale model uncertainty on identification of optimal catalyst properties.\n \n \n \n\n\n \n Ulissi, Z. W.; Prasad, V.; and Vlachos, D.\n\n\n \n\n\n\n Journal of Catalysis, 281(2): 339–344. 7 2011.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{ISI:000293422100015,\n  Title                    = {Effect of multiscale model uncertainty on identification of optimal catalyst properties},\n  Author                   = {Ulissi, Zachary W. and Prasad, Vinay and Vlachos, Dionisios},\n  Journal                  = {Journal of Catalysis},\n  Year                     = {2011},\n\n  Month                    = {7},\n  Number                   = {2},\n  Pages                    = {339--344},\n  Volume                   = {281},\n\n  Abstract                 = {Computer-based catalyst design has been a long standing dream of the chemistry community for replacing tedious and expensive experimental trial-and-error. While first-principle kinetic modeling emerges as a powerful tool for catalyst selection, it has mainly been limited to using a single catalyst descriptor, simplified chemical kinetic models, and assumptions that question the predictive capability of computational results in the absence of addressing the effect of error in kinetic parameters. Here, we introduce a new framework to address the effect of model uncertainty on optimal catalyst property identification. The framework is applied to the ammonia decomposition reaction for CO-free H(2) production for fuel cells. It is shown that a range of materials, rather than a single material, should be experimentally screened. Among kinetic model parameters, the often neglected adsorbate-adsorbate interactions can have a profound effect on catalyst selection. The importance of lateral interactions is confirmed with recent experimental data. (C) 2011 Elsevier Inc. All rights reserved.},\n  Doi                      = {10.1016/j.jcat.2011.05.019},\n  ISSN                     = {0021-9517},\n  Unique-id                = {ISI:000293422100015}\n}\n\n

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\n \n\n \n \n \n \n \n Carbon Nanotubes as Molecular Conduits: Advances and Challenges for Transport through Isolated Sub-2~nm Pores.\n \n \n \n\n\n \n Ulissi, Z. W.; Shimizu, S.; Lee, C. Y.; and Strano, M. S.\n\n\n \n\n\n\n Journal of Physical Chemistry Letters, 2(22): 2892–2896. 11 2011.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{ISI:000297195600011,\n  Title                    = {Carbon Nanotubes as Molecular Conduits: Advances and Challenges for Transport through Isolated Sub-2~{nm} Pores},\n  Author                   = {Ulissi, Zachary W. and Shimizu, Steven and Lee, Chang Young and Strano, Michael S.},\n  Journal                  = {Journal of Physical Chemistry Letters},\n  Year                     = {2011},\n\n  Month                    = {11},\n  Number                   = {22},\n  Pages                    = {2892--2896},\n  Volume                   = {2},\n\n  Abstract                 = {Devices that explore transport through the narrowest diameter single-walled carbon nanotubes (SWCNTs) have only recently been enabled by advances in SWCNT synthesis methods and experimental design. These devices hold promise as next-generation sensors, platforms for water desalination, proton conduction, energy storage, and to directly probe molecular transport under significant geometric confinement In this Perspective, we first describe this new generation of devices and then highlight two important concepts that have emerged from recent work. First, the most reliable way to identify transport is to borrow techniques from the biological and silicon nanopore communities and analyze the discrete stochastic events caused by molecules blocking the SWCNT channel. Second, it is nearly impossible to isolate mass transport within a SWCNT without a substantial suppression of leakage transport and around the SWCNT. To highlight this, we discuss experiments showing water transport along the exterior of SWCNTs. Finally, we describe some further innovations to these devices in the near future that will allow for a more complete understanding of confined molecular transport.},\n  Doi                      = {10.1021/jz201136c},\n  ISSN                     = {1948-7185},\n  Researcherid-numbers     = {Lee, Chang Young/E-3793-2010},\n  Unique-id                = {ISI:000297195600011}\n}\n\n

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\n \n\n \n \n \n \n \n Applicability of Birth-Death Markov Modeling for Single-Molecule Counting Using Single-Walled Carbon Nanotube Fluorescent Sensor Arrays.\n \n \n \n\n\n \n Ulissi, Z. W.; Zhang, J.; Boghossian, A. A.; Reuel, N. F.; Shimizu, S. F. E.; Braatz, R. D.; and Strano, M. S.\n\n\n \n\n\n\n Journal of Physical Chemistry Letters, 2(14): 1690–1694. 7 2011.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@Article{ISI:000293191800009,\n  Title                    = {Applicability of Birth-Death {Markov} Modeling for Single-Molecule Counting Using Single-Walled Carbon Nanotube Fluorescent Sensor Arrays},\n  Author                   = {Ulissi, Zachary W. and Zhang, Jingqing and Boghossian, Ardemis A. and Reuel, Nigel F. and Shimizu, Steven F. E. and Braatz, Richard D. and Strano, Michael S.},\n  Journal                  = {Journal of Physical Chemistry Letters},\n  Year                     = {2011},\n\n  Month                    = {7},\n  Number                   = {14},\n  Pages                    = {1690--1694},\n  Volume                   = {2},\n\n  Abstract                 = {In recent work, we have shown that d(AT)(15) DNA-wrapped single-walled carbon nanotubes (SWNTs) are able to detect the adsorption and desorption of single molecules of nitric oxide (NO) from the surface by quenching of the near-infrared fluorescence (Zhang et al. J. Am. Chem. Soc. 2011, 133, 567-581). A central question is how to estimate the local concentration from stochastic dynamics for these types of sensors. Herein, we employ an exact solution to the birth-death Markov model to estimate the local analyte concentration from the stochastic dynamics. Conditions are derived for the intrinsic variance displayed by identical sensor elements, and the homogeneity of the environment is assessed by comparing experimental sensor-to-sensor variance with this limit. We find that d(AT)(15) DNA-wrapped SWNTs demonstrate variances that are close to the idealized limit at relatively high NO concentrations (19.4 mu M). At 780 nM, the sensor-to-sensor variance is approximately double the idealized value, indicating marginal variation in the SWNT array. An NO adsorption coefficient of 2.6 x 10(-4) {[}mu M(-1)] is identified, and we outline how to predict the local analyte concentration from the sensor dynamics.},\n  Doi                      = {10.1021/jz200572b},\n  ISSN                     = {1948-7185},\n  Unique-id                = {ISI:000293191800009}\n}\n\n

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\n \n 2010\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n High throughput multiscale modeling for design of experiments, catalysts, and reactors: Application to hydrogen production from ammonia.\n \n \n \n\n\n \n Prasad, V.; Karim, A.; Ulissi, Z. W.; Zagrobelny, M.; and Vlachos, D.\n\n\n \n\n\n\n Chemical Engineering Science, 65(1, SI): 240–246. 1 2010.\n \n\n\n\n
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@Article{ISI:000276206700038,\n  Title                    = {High throughput multiscale modeling for design of experiments, catalysts, and reactors: Application to hydrogen production from ammonia},\n  Author                   = {Prasad, Vinay and Karim, Ayman and Ulissi, Zachary W. and Zagrobelny, Megan and Vlachos, Dionisios},\n  Journal                  = {Chemical Engineering Science},\n  Year                     = {2010},\n\n  Month                    = {1},\n  Number                   = {1, SI},\n  Pages                    = {240--246},\n  Volume                   = {65},\n\n  Abstract                 = {A novel approach for design of experiments (DOE) is outlined that combines high throughput multiscale modeling, sensitivity analysis, and information extraction from massive computational data using informatics tools. This approach is implemented by conducting experiments of ammonia decomposition on a Ru/gamma-Al(2)O(3) catalyst in a fixed bed reactor. It is shown that a relatively small number of experiments chosen from this new DOE approach can enable refinement of microkinetic models and render them predictive over the (large) experimentally important parameter space. Microkinetic models are subsequently used for process and product design. Specifically, a membrane fixed bed reactor is simulated and is shown to outperform the conventional fixed bed reactor at intermediate temperatures for hydrogen production. Also, the attributes of the best catalyst for ammonia decomposition are identified as a function of processing conditions. It is shown that for NH(3) decomposition, processing conditions do not significantly affect the best catalyst choice. In contrast, fundamental physicochemical phenomena, such as adsorbate adsorbate interactions, can have a profound effect on catalyst discovery. (C) 2009 Elsevier Ltd. All rights reserved.},\n  Doi                      = {10.1016/j.ces.2009.05.054},\n  ISSN                     = {0009-2509},\n  Orcid-numbers            = {Karim, Ayman/0000-0001-7449-542X},\n  Researcherid-numbers     = {Karim, Ayman/G-6176-2012},\n  Unique-id                = {ISI:000276206700038}\n}\n\n

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\n \n\n \n \n \n \n \n Visualization of biological texture using correlation coefficient images.\n \n \n \n\n\n \n Sviridov, A. P; Ulissi, Z. W.; Chernomordik, V. V; Hassan, M.; and Gandjbakhche, A. H\n\n\n \n\n\n\n Journal of Biomedical Optics, 11(6): 060504. 2006.\n \n\n\n\n
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@article{sviridov2006visualization,\n  title={Visualization of biological texture using correlation coefficient images},\n  author={Sviridov, Alexander P and Ulissi, Zachary W. and Chernomordik, Victor V and Hassan, Moinuddin and Gandjbakhche, Amir H},\n  journal={Journal of Biomedical Optics},\n  volume={11},\n  number={6},\n  pages={060504},\n  year={2006},\n  doi={10.1117/1.2400248},\n  publisher={International Society for Optics and Photonics}\n}\n

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