Textured Asymmetric Membrane Electrode Assemblies of Piezoelectric Phosphorene and Ti_(3)C_(2)T_(x)MXene Heterostructures for Enhanced Electrochemical Stability and Kinetics in LIBs

Black phosphorus with a superior theoretical capacity(2596 mAh g^(-1))and high conductivity is regarded as one of the powerful candidates for lithium-ion battery(LIB)anode materials,whereas the severe volume expansion and sluggish kinetics still impede its applications in LIBs.By contrast,the exfoliated two-dimensional phosphorene owns negligible volume variation,and its intrinsic piezoelectricity is considered to be beneficial to the Li-ion transfer kinetics,while its positive influence has not been discussed yet.Herein,a phosphorene/MXene heterostructure-textured nanopiezocomposite is proposed with even phosphorene distribution and enhanced piezo-electrochemical coupling as an applicable free-standing asymmetric membrane electrode beyond the skin effect for enhanced Li-ion storage.The experimental and simulation analysis reveals that the embedded phosphorene nanosheets not only provide abundant active sites for Li-ions,but also endow the nanocomposite with favorable piezoelectricity,thus promoting the Li-ion transfer kinetics by generating the piezoelectric field serving as an extra accelerator.By waltzing with the MXene framework,the optimized electrode exhibits enhanced kinetics and stability,achieving stable cycling performances for 1,000 cycles at 2 A g^(-1),and delivering a high reversible capacity of 524 m Ah g^(-1)at-20℃,indicating the positive influence of the structural merits of self-assembled nanopiezocomposites on promoting stability and kinetics.

Improving Both Electron and Hole Mobilities of an Ambipolar Polymer by Integrating Sodium Sulfonate-Tethered Alkyl Side Chains

of main observation and conclusion High performance ambipolar organic semiconductors are highly desirable for organic logic circuits.Herein,we demonstrate the integration of sodium sulfonate(SS)-tethered sidechains into a diketopyrrolopyrrole-based ambipolar polymer(PDPP3T)can simultaneously improve its hole and electron transport performances either parallel or perpendicular to polymer film.Three SS-functionalized polymers(PDPP3T-XSS,x=0.025,0.05 and 0.10)were synthesized and studied.It was found that SS functionalization can reinforce interchain n-Ti interactions,slightly lower frontier orbital energy levels,produce more rod-like structures in film,and change chain-packing from edge-on to face-on fashion,but has little influence on thermal properties.More interestingly,organic field-effect transistors reported hole mobility of 0.27±0.066 cm^2.V^-1·s^-1 and electron mobility of 0.038±0.016 cm^2.V^-1·s^-1 for PDPP3T,while increased 2.4 and 5 folds to 0.64±0.087 and 0.19±0.051 cm^2.V^-1·s^-1 for PDPP3T-0.025SS,respectively.Moreover,PDPP3T-xSS devices displayed reduced threshold voltages for both hole and electron transports.Meanwhile,space charge-limited current method found SS functionalization achieved an order of magnitude increase in electron mobility and slight enhancement in hole mobility transporting perpendicular to polymer film.In-depth investigations suggest such enhancements may originate from the joint actions of chain-stacking modulation and ionic doping effect.

Interfacial energy barrier tuning of hierarchical Bi_(2)O_(3)/WO_(3) heterojunctions for advanced triethylamine sensor

Traditional triethylamine(TEA)sensors suffer from the drawback of serious cross-sensitivity due to the low charge-transfer ability of gas-sensing materials.Herein,an advanced anti-interference TEA sensor is designed by utilizing interfacial energy barriers of hierarchical Bi_(2)O_(3)/WO_(3) composite.Benefiting from abundant slit-like pores,desirable defect features,and amplification effect of heterojunctions,the sensor based on Bi_(2)O_(3)/WO_(3) composite with 40%Bi_(2)O_(3)(0.4-Bi_(2)O_(3)/WO_(3))demonstrates remarkable performance in terms of faster response/recovery time(1.7-fold/1.2-fold),higher response(2.1-fold),and lower power consumption(30℃-decrement)as compared with the pristine WO_(3) sensor.Furthermore,the composite sensor exhibits long-term stability,reproducibility,and negligible response towards interfering molecules,indicating the promising potential of Bi_(2)O_(3)/WO_(3) heterojunctions in anti-interference detection of low-concentration TEA in real applications.This work not only offers a rational solution to design advanced gas sensors by tuning the interfacial energy barriers of heterojunctions,but also provides a fundamental understanding of hierarchical Bi_(2)O_(3) structures in the gas-sensing field.

Experimental search for high-performance ferroelectric tunnel junctions guided by machine learning

Ferroelectric tunnel junction(FTJ)has attracted considerable attention for its potential applications in nonvolatile memory and neuromorphic computing.However,the experimental exploration of FTJs with high ON/OFF ratios is a challenging task due to the vast search space comprising of ferroelectric and electrode materials,fabrication methods and conditions and so on.Here,machine learning(ML)is demonstrated to be an effective tool to guide the experimental search of FTJs with high ON/OFF ratios.A dataset consisting of 152 FTJ samples with nine features and one target attribute(i.e.,ON/OFF ratio)is established for ML modeling.Among various ML models,the gradient boosting classification model achieves the highest prediction accuracy.Combining the feature importance analysis based on this model with the association rule mining,it is extracted that the utilizations of{graphene/graphite(Gra)(top),LaNiO_(3)(LNO)(bottom)}and{Gra(top),Ca_(0.96)Ce_(0.04)MnO_(3)(CCMO)(bottom)}electrode pairs are likely to result in high ON/OFF ratios in FTJs.Moreover,two previously unexplored FTJs:Gra/BaTiO_(3)(BTO)/LNO and Gra/BTO/CCMO,are predicted to achieve ON/OFF ratios higher than 1000.Guided by the ML predictions,the Gra/BTO/LNO and Gra/BTO/CCMO FTJs are experimentally fabricated,which unsurprisingly exhibit≥1000 ON/OFF ratios(~8540 and~7890,respectively).This study demonstrates a new paradigm of developing high-performance FTJs by using ML.