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

Open Journal Vehicular Technology 2024 2025
Samuel Thornton, Nithin Santhanam, Rajeev Chhajer, Sujit Dey
IEEE Conference on Decision and Control (CDC) 2024
Sooyung Byeon, Danyang Tian, Jackie Ayoub, Miao Song, Ehsan Moradi Pari, Inseok Hwang
Neural Information Processing Systems (NeurIPS), 2024. 2024
Seunggeun Chi, Pin-Hao Huang, Enna Sachdeva, Hengbo Ma, Karthik Ramani, Kwonjoon Lee
NeurIPS 2024 2024
Huao Li, Hossein Nourkhiz Mahjoub, Behdad Chalaki, Vaishnav Tadiparthi, Kwonjoon Lee, Ehsan Moradi-Pari, Charles Michael Lewis, Katia P. Sycara
Robotics and Automation Letters (RA-L) 2024
Jinning Li, Jiachen Li, Sangjae Bae, and David Isele
Conference on Robot Learning (CoRL) 2024 Learning Robot Fine and Dexterous Manipulation Workshop 2024
Thomas Power, Abhinav Kumar, Fan Yang, Sergio Aguilera Marinovic, Soshi Iba, Rana Soltani Zarrin, Dmitry Berenson
Empirical Methods in Natural Language Processing (EMNLP 2024) 2024
Muhan Lin, Shuyang Shi, Yue Guo, Behdad Chalaki, Vaishnav Tadiparthi, Simon Stepputtis, Joseph Campbell, Katia P. Sycara, Ehsan Moradi-Pari
Frontiers in Robotics and Automation 2024
Hifza Javed, Weinan Wang, Affan Bin Usman, and Nawid Jamali
International Journal on Robotics Research 2024
Muchen Sun, Francesca Baldini, Pete Trautman, Todd Murphey
Robotics and Automation Letters (RA-L) 2024
Mansur M. Arief, Mike Timmerman, Jiachen Li, David Isele, and Mykel J. Kochenderfer
Nat. Commun. 15, 10080 (2024) 2024
Xufan Li, Samuel Wyss, Emanuil Yanev, Qing-Jie Li, Shuang Wu, Yongwen Sun, Raymond R. Unocic, Joseph Stage, Matthew Strasbourg, Lucas M. Sassi, Yingxin Zhu, Ju Li, Yang Yang, James Hone, Nicholas Borys, P. James Schuck, Avetik R. Harutyunyan
NeurIPS 2024 Workshop Open-World Agents 2024
Nikki_Lijing_Kuang, Songpo Li, Soshi Iba
Intelligent Robots and Systems (IROS) 2024
Hongyu Li, Snehal Dikhale, Jinda Cui, Soshi Iba, and Nawid Jamali
IROS 2024 2024
Viet-Anh Le​, Vaishnav Tadiparthi, Behdad Chalaki,​ Hossein Nourkhiz Mahjoub, Jovin D’sa, Ehsan Moradi-Pari​
arXiv preprint arXiv:2409.09415 (2024) 2024
Lingo, Ryan, Martin Arroyo, and Rajeev Chhajer
Conference on Robot Learning (CoRL) 2024
Patrick Naughton, Jinda Cui, Karankumar Patel, and Soshi Iba
European Conference on Computer Vision (ECCV), 2024 2024
Yuchen Yang, Kwonjoon Lee, Behzad Dariush, Yinzhi Cao, Shao-Yuan Lo
European Conference on Computer Vision (ECCV), 2024 2024
Seunggeun Chi, Hyung-gun Chi, Hengbo Ma, Nakul Agarwal, Faizan Siddiqui, Karthik Ramani, Kwonjoon Lee
European Conference on Computer Vision (ECCV), 2024 2024
Shijie Wang, Qi Zhao, Minh Quan Do, Nakul Agarwal, Kwonjoon Lee, Chen Sun