Ion–Electron Coupling Enables Ionic Thermoelectric Material with New Operation Mode and High Energy Density

Ionic thermoelectrics(i-TE) possesses great potential in powering distributed electronics because it can generate thermopower up to tens of millivolts per Kelvin. However,as ions cannot enter external circuit, the utilization of i-TE is currently based on capacitive charge/discharge, which results in discontinuous working mode and low energy density. Here,we introduce an ion–electron thermoelectric synergistic(IETS)effect by utilizing an ion–electron conductor. Electrons/holes can drift under the electric field generated by thermodiffusion of ions, thus converting the ionic current into electrical current that can pass through the external circuit. Due to the IETS effect, i-TE is able to operate continuously for over 3000 min.Moreover, our i-TE exhibits a thermopower of 32.7 mV K^(-1) and an energy density of 553.9 J m^(-2), which is more than 6.9 times of the highest reported value. Consequently, direct powering of electronics is achieved with i-TE. This work provides a novel strategy for the design of high-performance i-TE materials.

Artificial intelligence-assisted colorimetry for urine glucose detection towards enhanced sensitivity,accuracy,resolution,and anti-illuminating capability

Colorimetry often suffers from deficiency in quantitative determination,susceptibility to ambient illuminance,and low sensitivity and visual resolution to tiny color changes.To offset these deficiencies,we incorporate deep machine learning into colorimetry by introducing a convolutional neural network(CNN)with powerful parallel processing,self-organization,and self-learning capabilities.As a proof of concept,a plasmonic nanosensor is proposed for the colorimetric detection of glucose by coupling Benedict’s reagent with gold nanoparticles(AuNPs),which relies on the assemble of AuNPs into dendritic nanochains by Cu2O.The distinct difference of refractive index between Cu2O and Au and the localized surface plasmon resonance coupling effect among AuNPs leads to a broad spectral shift as well as abundant color changes,thereby providing sufficient data for selflearning enabled by machine learning.The CNN is then used to fully diversify the learning and training of the images from standard samples under different ambient conditions and to obtain a classifier that can not only recognize tiny color changes that are imperceptible to human eyes,but also exhibit high accuracy and excellent anti-environmental interference capability.This classifier is then compiled as an application(APP)and implanted into a smartphone with Android environment.306 clinical urine samples were detected using the proposed method and the results showed a satisfactory correlation(87.6%)with that of a standard blood glucose test method.More importantly,this method can be generalized to other applications in colorimetry,and more broadly,in other scientific domains that involve image analysis and quantification.