Electrical conductivity of carbon nanostructures - Honda Research Institute USA

Electrical conductivity of carbon nanostructures

Electrical conductivity of carbon nanostructures

The core of this project is for atomistic level understanding of environmental impact on conductivities of SWNTs and 2-D materials in order to reveal their ultimate sensitivities.

The advance of nanotechnology has opened new opportunities to develop ever more sensitive sensors. Single-walled carbon nanotube (SWNT) is one of the most exciting materials in this regard due to its high surface-to-volume ratio and unique electronic structure. Ironically the ultrahigh sensitivity of SWNTs is inevitably compromised by various unintentional contaminants from the device fabrication process as well as the ambient environment. Indeed, our studies showed that despite significant progress made in the last decade, we are still nowhere close to what a pristine SWNT-sensor can truly offer. Under in situ ultraviolet (UV) light illumination, the electrical response of pristine SWNTs became extremely sensitive to gaseous molecules such as nitric oxide (NO) with a detection limit as low as 590 parts-per-quadrillion (ppq) at room temperature, which is ~10,000 times superior to any reported sensor to date. Gas sensing on NO2 and NH3 further confirmed that pristine nanotubes could have sensitivities orders of magnitude better than what previously had achieved. Furthermore, we observed the electrical response to reverse its direction upon NH3 exposure after applying in situ UV light illumination, which gave us clear indications of the origin of gas ultrasensitivity. Because of its simplicity the concept of in situ cleaning can be deployed in existing sensor architectures. The study is expected to pave the way for developing the next generation of ultrasensitive sensors.

At the core of the detection sensibility of the sensors produced at our lab was the change in conductivity produced by the exposure of the nanotubes to the molecules of NO, NO2, and NH3. Given that the films were a network of tubes and junctions, it was reasonable to assume that the total conductivity of the film was the combination of the intrinsic transport response of the tubes and the less defined contribution from the junctions. Our first exploration of these questions was done on the study of the gases on the intrinsic transport characteristics of the tubes. The effects of the molecules on the transport response of the tubes arises from the combined effect of doping, through the injection of carriers, and the introduction of scattering centers affecting the transport process. These are not independent effects. On the contrary, they are both the result of the mechanisms of interaction between the molecules and the tubes. As a consequence, our theoretical modeling consisted of a series of carefully planned ab-initio calculations followed by their theoretical interpretation in order to obtain general knowledge of the charge transfer and scattering mechanisms governing the transport process in the pristine tubes.

The calculation was based on the kinetic Langmuir model, with the addition of poison sites, from which no desorption occurs during the experiment. The model reproduced the available experimental data very well. It not only explains the microscopic phenomena governing adsorption, but also the model will guide us the direction to improve the sensitivity and detection limit of the sample. Further experimental work on the effect of temperature, UV light, gate voltage, and defect density can potentially further improve their performance and achieve the ultimate sensitivities of those gas sensors.

Publications

Findings of the Association for Computational Linguistics: ACL 2025. 2025
Lingjun Zhao, Mingyang Xie, Paola Cascante-Bonilla, Hal Daumé III, Kwonjoon Lee
Findings of the Association for Computational Linguistics: ACL 2025. 2025
Huaizhi Qu, Xinyu Zhao, Jie Peng, Kwonjoon Lee, Behzad Dariush, Tianlong Chen
International Conference on Machine Learning (ICML), 2025 [Spotlight, top 2.6% submittions] 2025
Chunhui Zhang, Zhongyu Ouyang, Kwonjoon Lee, Nakul Agarwal, Sean Dae Houlihan, Soroush Vosoughi, Shao-Yuan Lo
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. 2025
Haoqiang Kang, Enna Sachdeva, Piyush Gupta, Sangjae Bae, Kwonjoon Lee
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025 [Highlight, top 3.7% submittions] 2025
Bardia Safaei, Faizan Siddiqui, Jiacong Xu, Vishal M. Patel, Shao-Yuan Lo
Robotics Science and Systems (RSS), 2025 2025
Sirui Chen, Sergio Francisco Aguilera Marinovic, Soshi Iba, Rana Soltani Zarrin
International Journal of Computer Vision (IJCV) [IF=11.6] 2025
Yuxiang Guo, Faizan Siddiqui, Yang Zhao, Rama Chellappa, Shao-Yuan Lo
RSS 2025 - Workshop on Learned Robot Representations 2025
Fan Yang, Sergio Francisco Aguilera Marinovic, Soshi Iba, Rana Soltani Zarrin, Dmitry Berenson
CVPRW 2025 2025
Zhihao Zhao, Reza Ghoddoosian, Isht Dwivedi, Nakul Agarwal, Behzad Dariush
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025 [Highlight, top 3.7% submittions] 2025
Jiacong Xu, Shao-Yuan Lo, Bardia Safaei, Vishal M. Patel, Isht Dwivedi
Robotics Science and Systems (RSS) 2025 - Workshop on Out-of-Distribution Generalization in Robotics 2025
Zifan Zhao, Siddhant Haldar, Jinda Cui, Lerrel Pinto,
IEEE International Conference on Learning Representations (ICRA), 2025. 2025
Piyush Gupta, David Isele, Enna Sachdeva, Pin-Hao Huang, Behzad Dariush, Kwonjoon Lee, Sangjae Bae
Physical Review Letters 134, 183603 (2025) 2025
Hanfeng Wang, Shuang Wu, Kurt Jacobs, Yuqin Duan, Dirk R Englund, Matthew E Trusheim
ICRA 2025 2025
David Isele, Alexandre Miranda A˜non, Faizan M. Tariq, Goro Yeh, Avinash Singh, and Sangjae Bae
IEEE International Conference on Robotics and Automation (ICRA), 2025, 2025
Abhinav Kumar, Thomas Power, Fan Yang, Sergio Aguilera Marinovic, Soshi Iba, Rana Soltani Zarrin, Dmitry Berenson
ICRA 2025 2025
Max Muchen Sun, Peter Trautman, and Todd Murphey
NAFEMS World Congress; 2025 2025
Ali Nassiri, Phillip Aquino, Allen Sheldon, Sogol Lotfi, Duane Detwiler
International Conference on Learning Representations (ICLR), 2025 2025
Hongxin Zhang, Zeyuan Wang, Qiushi Lyu, Zheyuan Zhang, Sunli Chen, Tianmin Shu, Behzad Dariush, Kwonjoon Lee, Yilun Du, Chuang Gan
Ohio State Materials and Manufacturing Conference 2025
Phillip Aquino
International Conference on Acoustics, Speech and Signal Processing 2025
Abinay Reddy Naini, Zhaobo Zheng, Teruhisa Misu, Kumar Akash