Solution-processed wafer-scale nanoassembly of conducting polymers enables selective ultratrace nerve agent detection at low power

There is a great interest in developing microelectronic devices based on nanostructured conducting polymers that can selectively electro-couple analytes at high sensitivity and low power.Nanostructured conducting polymers have emerged as promising candidates for this technology due to their excellent stability with low redox potential,high conductivity,and selectivity endowed by chemical functionalization.However,it remains challenging to develop cost-effective and large-scale assembly approaches for functionalized conducting polymers in the practical fabrication of electronic devices.Here,we reported a straightforward waferscale assembly of nanostructured hexafluoroisopropanol functionalized poly(3,4-ethylenedioxythiophene)(PEDOT-HFIP)on smooth substrates.This approach is template-free,solution-processed,and adaptable to conductive and nonconductive substrates.By this approach,the nanostructured PEDOT-HFIPs could be easily integrated onto interdigitated electrodes with intimate ohmic contact.At the optimized space-to-volume ratio,we demonstrated a low-power,sensitive,and selective nerve agent sensing technology using this platform by detecting sarin vapor with a limit of detection(LOD)of 10 ppb and signal strength of 400 times the water interference at the same concentration,offering significant advantages over existing similar technologies.We envision that its easy scale-up,micro size,small power consumption,and combination of high sensitivity and selectivity make it attractive for various wearable platforms.

Development and Implementation of Physics-Informed Neural ODE for Dynamics Modeling of a Fixed-Wing Aircraft Under Icing/Fault

Accurate dynamics modeling is crucial for the safety and control offixed-wing aircraft under perturbation(e.g.icing/fault).In this work,we propose a physics-informed Neural Ordinary Differential Equation(PI-NODE)-based scheme for aircraft dynamics modeling under icing/fault.First,icing accumulation and control surface faults are considered and injected into the nominal(clean)aircraft dynamics model.Second,the physics knowledge of aircraft dynamics modeling is divided into kinematics and kinetics.The former is universally applicable and borrows directly from the nominal aircraft.The latter kinetics knowledge,which hinges on external forces and moments,is inaccurate and challenging under icing/fault.To address this issue,we employ Neural ODE to compensate for the residual between the aircraft dynamics under icing/fault and the nominal(clean)condition,resulting in a naturally continuous-time modeling approach.In experiments,we benchmark the proposed PI-NODE against three baseline methods in a dedicated flight scenario.Comparative studies validate the higher accuracy and improve the generalization ability of the proposed PI-NODE for aircraft dynamics modeling under icing/fault.