Impacts of thermal and electric contact resistance on the material design in segmented thermoelectric generators

Segmented thermoelectric generators(STEGs)can exhibit present superior performance than those of the conventional thermoelectric generators.Thermal and electrical contact resistances exist between the thermoelectric material interfaces in each thermoelectric leg.This may significantly hinder performance improvement.In this study,a five-layer STEG with three pairs of thermoelectric(TE)materials was investigated considering the thermal and electrical contact resistances on the material contact surface.The STEG performance under different contact resistances with various combinations of TE materials were analyzed.The relationship between the material sequence and performance indicators under different contact resistances is established by machine learning.Based on the genetic algorithm,for each contact resistance combination,the optimal material sequences were identified by maximizing the electric power and energy conversion efficiency.To reveal the underlying mechanism that determines the heat-to-electrical performance,the total electrical resistance,output voltage,ZT value,and temperature distribution under each optimized scenario were analyzed.The STEG can augment the heat-to-electricity performance only at small contact resistances.A large contact resistance significantly reduces the performance.At an electrical contact resistance of RE=10^(-3) K⋅m^(2)⋅W^(-1) and thermal contact resistance of RT=10-8Ω⋅m^(2),the maximum electric power was reduced to 5.71 mW(90.86 mW without considering the contact resistance).And the maximum energy conversion efficiency is lowered to 2.54%(12.59%without considering the contact resistance).

A co-training style semi-supervised artificial neural network modeling and its application in thermal conductivity prediction of polymeric composites filled with BN sheets

Predicting the thermal conductivity of polymeric composites filled with BN sheets is helpful for fabricating ther-mal management material.In this study,a co-training style semi-supervised artificial neural network model(Co-ANN)was proposed to take advantage of unlabeled data to refine the prediction.The thermal conductivity of polymer matrix,the diameter,aspect ratio,and volume fraction of the BN sheets are considered as the input variables of the thermal conduction model.Two artificial neural network(ANN)learners with different archi-tecture will label the unlabeled examples.Through estimating the labeling confidence from the mathematical influence and thermal conductive behavior,the most confidently labeled example will be used to augment the training dataset.The lower limit of the labeling confidence is introduced to reduce the data noise.After learn-ing the augmented training information,a combination of two ANN regressors will construct the final Co-ANN thermal conduction model.Compared to other models,the newly developed Co-ANN thermal conduction model remarkably improves the thermal conductivity prediction and exhibits the best accuracy and generalization per-formance.The proposed method shows a vast potential in thermal conductive material design.

Pathogenesis of anti-melanoma differentiation-associated gene-5(MDA5)dermatomyositis

Dermatomyositis(DM)is an idiopathic inflammatory myopathy,with typical cutaneous manifestations and muscle lesions,usually involving multiple organs.Patients with antimelanoma differentiation-associated gene-5(MDA5)DM usually present with clinical amyopathic DM(CADM),which is characterized by a series of unique skin and systemic manifestations,including skin ulcer and interstitial lung disease(ILD).Among them,ILD is the most common complication of anti-MDA5 DM,and in some cases,it can develop into rapidly progressive ILD(RP-ILD).^([1,2])