查看更多>>摘要：Simulated reality encompasses virtual,augmented,and mixed realities-each characterized by different degrees of truthfulness in the visual perception:"all false,""coexistence of true and false,"and"difficult distinction between true and false,'respectively.In all these technologies,however,the temperature rendering of virtual objects is still an unsolved problem.Undoubtedly,the lack of thermal tactile functions substantially reduces the quality of the user's real-experience perception.To address this challenge,we propose theoretically and realize experimentally a technological platform for the in situ simulation of thermal reality.To this purpose,we design a thermal metadevice consisting of a reconfigurable array of radiating units,capable of generating the thermal image of any virtual object,and thus rendering it in situ together with its thermal signature.This is a substantial technological advance,which opens up new possibilities for simulated reality and its applications to human activities.
查看更多>>摘要：The influence of a mechanical structure's volume increment on the volume power density(VPD)of triboelectric nanogenerators(TENGs)is often neglected when considering surface charge density and surface power density.This paper aims to address this gap by introducing a standardized VPD metric for a more comprehensive evaluation of TENG performance.The study specifically focuses on 2 frequency-up mechanisms,namely,the integration of planetary gears(PG-TENG)and the implementation of a double-cantilever structure(DC-TENG),to investigate their impact on VPD.The study reveals that the PG-TENG achieves the highest volume average power density,measuring at 0.92 W/m3.This value surpasses the DC-TENG by 1.26 times and the counterpart TENG by a magnitude of 69.9 times.Additionally,the PG-TENG demonstrates superior average power output.These findings introduce a new approach for enhancing TENGs by incorporating frequency-up mechanisms,and highlight the importance of VPD as a key performance metric for evaluating TENGs.
查看更多>>摘要：The identification of protein-protein interaction(PPI)sites is essential in the research of protein function and the discovery of new drugs.So far,a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites.However,existing methods suffer from the low predictive accuracy or the limited scope of application.Specifically,some methods learned only global or local sequential features,leading to low predictive accuracy,while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information.There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites.Herein,a novel ensemble framework for PPI sites prediction,EnsemPPIS,was therefore proposed based on transformer and gated convolutional networks.EnsemPPIS can effectively capture not only global and local patterns but also residue interactions.Specifically,EnsemPPIS was unique in(a)extracting residue interactions from protein sequences with transformer and(b)further integrating global and local sequential features with the ensemble learning strategy.Compared with various existing methods,EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks.Moreover,pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information.The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis.
查看更多>>摘要：Achieving color-tunable emission in single-component organic emitters with multistage stimuli-responsivenessisof vital significance for intelligent optoelectronic applications,but remains enormously challenging.Herein,we present an unprecedented example of a color-tunable single-component smart organic emitter(DDOP)that simultaneously exhibits multistage stimuli-responsiveness and multimode emissions.DDOP based on a highly twisted amide-bridged donor-acceptor-donor structure has been found to facilitate intersystem crossing,form multimode emissions,and generate multiple emissive species with multistage stimuli-responsiveness.DDOP pristine crystalline powders exhibit abnormal excitation-dependent emissions from a monomer-dominated blue emission centered at 470 nm to a dimer-dominated yellow emission centered at 550 nm through decreasing the ultraviolet(UV)excitation wavelengths,whereas DDOP single crystals show a wide emission band with a main emission peak at 585 nm when excited at different wavelengths.The emission behaviors of pristine crystalline powders and single crystals are different,demonstrating emission features that are closely related to the aggregation states.The work has developed color-tunable single-component organic emitters with simultaneous multistage stimuli-responsiveness and multimode emissions,which is vital for expanding intelligent optoelectronic applications,including multilevel information encryption,multicolor emissive patterns,and visual monitoring of UV wavelengths.
查看更多>>摘要：Photodeformable polymer materials have a far influence in the fields of flexibility and intelligence.The stimulation energy is converted into mechanical energy through molecular synergy.Among kinds of photodeformable polymer materials,liquid crystalline polymer(LCP)photodeformable materials have been a hot topic in recent years.Chromophores such as azobenzene,a-cyanostilbene,and 9,10-dithiopheneanthracene have been widely used in LCP,which are helpful for designing functional molecules to increase the penetration depth of light to change physical properties.Due to the various applications of photodeformable polymer materials,there are many excellent reports in intelligent field.In this review,we have systematized LCP containing azobenzene into 3 categories depending on the degree of crosslinking liquid crystalline elastomers,liquid crystalline networks,and linear LCPs.Other structural,typical polymer materials and their applications are discussed.Current issues faced and future directions to be developed for photodeformable polymer materials are also summarized.
查看更多>>摘要：The recently discoveredATi3Bi5(A=Cs,Rb)exhibitintriguing quantum phenomenaincludingsuperconductivity,electronic nematicity,and abundant topological states.ATi3Bi5 present promising platforms for studying kagome superconductivity,band topology,and charge orders in parallel with AV3Sb5.In this work,we comprehensively analyze various properties of ATi3Bi5 covering superconductivity under pressure and doping,band topology under pressure,thermal conductivity,heat capacity,electrical resistance,and spin Hall conductivity(SHC)using first-principles calculations.Calculated superconducting transition temperature(Tc)of CsTi3Bi5 and RbTi3Bi5 at ambient pressure are about 1.85 and 1.92 K.When subject to pressure,Tc of CsTi3Bi5 exhibits a special valley and dome shape,which arises from quasi-two-dimensional compression to three-dimensional isotropic compression within the context of an overall decreasing trend.Furthermore,Tc of RbTi3Bi5 can be effectively enhanced up to 3.09 K by tuning the kagome van Hove singularities(VHSs)and flat band through doping.Pressures can also induce abundant topological surface states at the Fermi energy(EF)and tune VHSs across EF.Additionally,our transport calculations are in excellent agreement with recent experiments,confirming the absence of charge density wave.Notably,SHC of CsTi3Bi5 can reach up to 226ђ·(e Ω-cm)-1 at EF.Our work provides a timely and detailed analysis of the rich physical properties for ATi3Bi5,offering valuable insights for further experimental verifications and investigations in this field.
查看更多>>摘要：Photocatalytic reduction of CO2 into fuels provides a prospective tactic for regulating the global carbon balance utilizing renewable solar energy.However,CO2 molecules are difficult to activate and reduce due to the thermodynamic stability and chemical inertness.In this work,we develop a novel strategy to promote the adsorption and activation of CO2 molecules via the rapid energy exchange between the photoinduced Br vacancies and CO2 molecules.Combining insitu continuous wave-electron paramagnetic resonance(cw-EPR)and pulsed EPR technologies,we observe that the spin-spin relaxation time(T2)of BiOBr is decreased by 198 ns during the CO2 photoreduction reaction,which is further confirmed by the broadened EPR linewidth.This result reveals that there is an energy exchange interaction between in situ formed Br vacancies and CO2 molecules,which promotes the formation of high-energy CO2 molecules to facilitate the subsequent reduction reaction.In addition,theoretical calculations indicate that the bended CO2 adsorption configuration on the surface of BiOBr with Br vacancies caused the decrease of the lowest unoccupied molecular orbital of the CO2 molecule,which makes it easier for CO2 molecules to acquire electrons and get activated.In situ diffuse reflectance infrared Fourier transform spectroscopy further shows that the activated CO2 molecules are favorably converted to key intermediates of COOH*,resulting in a CO generation rate of 9.1 pmol g-1 h-1 and a selectivity of 100％.This study elucidates the underlying mechanism of CO2 activation at active sites and deepens the understanding of CO2 photoreduction reaction.
查看更多>>摘要：Effective synthesis planning powered by deep learning(DL)can significantly accelerate the discovery of new drugs and materials.However,most DL-assisted synthesis planning methods offer either none or very limited capability to recommend suitable reaction conditions(RCs)for their reaction predictions.Currently,the prediction of RCs with a DL framework is hindered by several factors,including:(a)lack of a standardized dataset for benchmarking,(b)lack of a general prediction model with powerful representation,and(c)lack of interpretability.To address these issues,we first created 2 standardized RC datasets covering a broad range of reaction classes and then proposed a powerful and interpretable Transformer-based RC predictor named Parrot.Through careful design of the model architecture,pretraining method,and training strategy,Parrot improved the overall top-3 prediction accuracy on catalysis,solvents,and other reagents by as much as 13.44％,compared to the best previous model on a newly curated dataset.Additionally,the mean absolute error of the predicted temperatures was reduced by about 4 ℃.Furthermore,Parrot manifests strong generalization capacity with superior cross-chemical-space prediction accuracy.Attention analysis indicates that Parrot effectively captures crucial chemical information and exhibits a high level of interpretability in the prediction of RCs.The proposed model Parrot exemplifies how modern neural network architecture when appropriately pretrained can be versatile in making reliable,generalizable,and interpretable recommendation for RCs even when the underlying training dataset may still be limited in diversity.
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