TY - JOUR
T1 - Few- and single-molecule reservoir computing experimentally demonstrated with surface-enhanced Raman scattering and ion gating
AU - Nishioka, Daiki
AU - Shingaya, Yoshitaka
AU - Tsuchiya, Takashi
AU - Higuchi, Tohru
AU - Terabe, Kazuya
N1 - Publisher Copyright:
© 2024 American Association for the Advancement of Science. All rights reserved.
PY - 2024/3
Y1 - 2024/3
N2 - Molecule-based reservoir computing (RC) is promising for achieving low power consumption neuromorphic computing, although the information-processing capability of small numbers of molecules is not clear. Here, we report a few- and single-molecule RC that uses the molecular vibration dynamics in the para-mercaptobenzoic acid (pMBA) detected by surface-enhanced Raman scattering (SERS) with tungsten oxide nanorod/silver nanoparticles. The Raman signals of the pMBA molecules, adsorbed at the SERS active site of the nanorod, were reversibly perturbated by the application of voltage-induced local pH changes near the molecules, and then used to perform time-series analysis tasks. Despite the small number of molecules used, our system achieved good performance, including >95% accuracy in various nonlinear waveform transformations, 94.3% accuracy in solving a second-order nonlinear dynamic system, and a prediction error of 25.0 milligrams per deciliter in a 15-minute-ahead blood glucose level prediction. Our work provides a concept of few-molecular computing with practical computation capabilities.
AB - Molecule-based reservoir computing (RC) is promising for achieving low power consumption neuromorphic computing, although the information-processing capability of small numbers of molecules is not clear. Here, we report a few- and single-molecule RC that uses the molecular vibration dynamics in the para-mercaptobenzoic acid (pMBA) detected by surface-enhanced Raman scattering (SERS) with tungsten oxide nanorod/silver nanoparticles. The Raman signals of the pMBA molecules, adsorbed at the SERS active site of the nanorod, were reversibly perturbated by the application of voltage-induced local pH changes near the molecules, and then used to perform time-series analysis tasks. Despite the small number of molecules used, our system achieved good performance, including >95% accuracy in various nonlinear waveform transformations, 94.3% accuracy in solving a second-order nonlinear dynamic system, and a prediction error of 25.0 milligrams per deciliter in a 15-minute-ahead blood glucose level prediction. Our work provides a concept of few-molecular computing with practical computation capabilities.
UR - http://www.scopus.com/inward/record.url?scp=85186126694&partnerID=8YFLogxK
U2 - 10.1126/sciadv.adk6438
DO - 10.1126/sciadv.adk6438
M3 - Article
C2 - 38416821
AN - SCOPUS:85186126694
SN - 2375-2548
VL - 10
JO - Science Advances
JF - Science Advances
IS - 9
M1 - eadk6438
ER -