Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single...Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single fluorescent probe(BDP-CHD)for high-throughput screening of phosgene,DCP and volatile acyl chlorides.The probe underwent a covalent cascade reaction with phosgene to form boron dipyrromethene(BODIPY)with bright green fluorescence.By contrast,DCP,diphosgene and acyl chlorides can covalently assembled with the probe,giving rise to strong blue fluorescence.The probe has demonstrated high-throughput detection capability,high sensitivity,fast response(within 3 s)and parts per trillion(ppt)level detection limit.Furthermore,a portable platform based on BDP-CHD was constructed,which has achieved high-throughput discrimination of 16 analytes through linear discriminant analysis(LDA).Moreover,a smartphone adaptable RGB recognition pattern was established for the quantitative detection of multi-analytes.Therefore,this portable fluorescence sensing platform can serve as a versatile tool for rapid and high-throughput detection of toxic phosgene,DCP and volatile acyl chlorides.The proposed“one for more”strategy simplifies multi-target discrimination procedures and holds great promise for various sensing applications.展开更多
The lack of efficient crop phenotypic measurement methods has become a bottleneck in the field of breeding and precision cultivation.However,high-throughput and accurate phenotypic measurement could accelerate the bre...The lack of efficient crop phenotypic measurement methods has become a bottleneck in the field of breeding and precision cultivation.However,high-throughput and accurate phenotypic measurement could accelerate the breeding and improve the existing cultivation management technology.In view of this,this paper introduces a high-throughput crop phenotype measurement platform named the LQ-FieldPheno,which was developed by China National Agricultural Information Engineering Technology Research Centre.The proposed platform represents a mobile phenotypic high-throughput automatic acquisition system based on a field track platform,which introduces the Internet of Things(IoT)into agricultural breeding.The proposed platform uses the crop phenotype multisensor central imaging unit as a core and integrates different types of equipment,including an automatic control system,upward field track,intelligent navigation vehicle,and environmental sensors.Furthermore,it combines an RGB camera,a 6-band multispectral camera,a thermal infrared camera,a 3-dimensional laser radar,and a deep camera.Special software is developed to control motions and sensors and to design run lines.Using wireless sensor networks and mobile communication wireless networks of IoT,the proposed system can obtain phenotypic information about plants in their growth period with a high-throughput,automatic,and high time sequence.Moreover,the LQ-FieldPheno has the characteristics of multiple data acquisition,vital timeliness,remarkable expansibility,high-cost performance,and flexible customization.The LQ-FieldPheno has been operated in the 2020 maize growing season,and the collected point cloud data are used to estimate the maize plant height.Compared with the traditional crop phenotypic measurement technology,the LQ-FieldPheno has the advantage of continuously and synchronously obtaining multisource phenotypic data at different growth stages and extracting different plant parameters.The proposed platform could contribute to the research of crop phenotype,remote sensing,agronomy,and related disciplines.展开更多
Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laborat...Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing.Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing.Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing.Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.展开更多
Background:Tumor cell heterogeneity mediated drug resistance has been recognized as the stumbling block of cancer treatment.Elucidating the cytotoxicity of anticancer drugs at single-cell level in a high-throughput wa...Background:Tumor cell heterogeneity mediated drug resistance has been recognized as the stumbling block of cancer treatment.Elucidating the cytotoxicity of anticancer drugs at single-cell level in a high-throughput way is thus of great value for developing precision therapy.However,current techniques suffer from limitations in dynamically characterizing the responses of thousands of single cells or cell clones presented to multiple drug conditions.Methods:We developed a new microfluidics-based“SMART”platform that is Simple to operate,able to generate a Massive single-cell array and Multiplex drug concentrations,capable of keeping cells Alive,Retainable and Trackable in the microchambers.These features are achieved by integrating a Microfluidic chamber Array(4320 units)and a sixConcentration gradient generator(MAC),which enables highly efficient analysis of leukemia drug effects on single cells and cell clones in a high-throughput way.Results:A simple procedure produces 6 on-chip drug gradients to treat more than 3000 single cells or single-cell derived clones and thus allows an efficient and precise analysis of cell heterogeneity.The statistic results reveal that Imatinib(Ima)and Resveratrol(Res)combination treatment on single cells or clones is much more efficient than Ima or Res single drug treatment,indicated by the markedly reduced half maximal inhibitory concentration(IC50).Additionally,single-cell derived clones demonstrate a higher IC_(50) in each drug treatment compared to single cells.Moreover,primary cells isolated from two leukemia patients are also found with apparent heterogeneity upon drug treatment on MAC.Conclusions:This microfluidics-based“SMART”platform allows high-throughput single-cell capture and culture,dynamic drug-gradient treatment and cell response monitoring,which represents a new approach to efficiently investigate anticancer drug effects and should benefit drug discovery for leukemia and other cancers.展开更多
Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate predictio...Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate prediction of displacement and position of VIV in flexible risers remains challenging under actual marine conditions.This study presents a data-driven model for riser displacement prediction that corresponds to field conditions.Experimental data analysis reveals that the XGBoost algorithm predicts the maximum displacement and position with superior accuracy compared with Support vector regression(SVR),considering both computational efficiency and precision.Platform displacement in the Y-direction demonstrates a significant positive correlation with both axial depth and maximum displacement magnitude.The fourth point displacement exhibits the highest contribution to model prediction outcomes,showing a positive influence on maximum displacement while negatively affecting the axial depth of maximum displacement.Platform displacement in the X-and Y-directions exhibits competitive effects on both the riser’s maximum displacement and its axial depth.Through the implementation of XGBoost algorithm and SHapley Additive exPlanation(SHAP)analysis,the model effectively estimates the riser’s maximum displacement and its precise location.This data-driven approach achieves predictions using minimal,readily available data points,enhancing its practical field applications and demonstrating clear relevance to academic and professional communities.展开更多
In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology bas...In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.展开更多
The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str...The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.展开更多
This paper presents an overview of the recent developments in hybrid wind-wave energy.With the focus on floating concepts,the possible configurations introduced in the literature are categorized and depicted,and the m...This paper presents an overview of the recent developments in hybrid wind-wave energy.With the focus on floating concepts,the possible configurations introduced in the literature are categorized and depicted,and the main conclusions obtained from the references are summarized.Moreover,offshore wind and wave resources are discussed in terms of complementarity and supplementarity,offering a new perspective to developing hybrid wind-wave energy systems that look for synergies not limited to maximizing power output.Then,the feasibility of the concepts under development is discussed in detail,with focus on technical feasibility,dynamic feasibility and limitations of the methods employed.The hybrid configurations that surpassed the experimental validation phase are highlighted,and the experimental results are summarized.By compiling more than 40 floating wind turbine concepts,new relations are drawn between power,wind turbine dimensions,platforms’draft and displacement,which are further related to the payload allowance of the units to accommodate wave devices and onboard power take-off systems.Bearing in mind that it is a challenge to model the exact dynamics of hybrid floating wind-wave platforms,this paper elucidates the current research gaps,limitations and future trends in the field.Lastly,based on the overview and topics discussed,several major conclusions are drawn concerning hybrid synergies,dynamics and hydrodynamics of hybrid platforms,feasibility of concepts,among other regards.展开更多
Purpose–This study summarizes the overall situation of the resources of the national science and technology innovation platform in the railway industry,including the distribution of platform types,supporting institut...Purpose–This study summarizes the overall situation of the resources of the national science and technology innovation platform in the railway industry,including the distribution of platform types,supporting institutions,construction sites,professional fields,etc.,to provide a reference for the further improvement and optimization of the national science and technology innovation platform system in the railway industry.Design/methodology/approach–Through literature review,field investigation,expert consultation and other methods,this paper systematically investigates and analyzes the development status of the national science and technology innovation platform in the railway industry.Findings–Taking the national science and technology innovation platform of the railway industry as the research object,this paper investigates and analyzes the construction,development and distribution of the national science and technology innovation platform of railway industry over the years.And the National Engineering Research Center of High-speed Railway and Urban Rail Transit System Technology was taken as an example to introduce its operation effect.Originality/value–China Railway has made great development achievements,with the construction and development of national science and technology innovation platform in the railway industry.In recent years,a large number of national science and technology innovation platforms have been built in the railway industry,which play an important role in railway technological innovation,standard setting and commodification,and Railway Sciences provide strong support for railway technology development.展开更多
This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model...This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model follows a“three-stage”and“two-subject”framework,incorporating a structured design for teaching content and assessment methods before,during,and after class.Practical results indicate that this approach significantly enhances teaching effectiveness and improves students’learning autonomy.展开更多
面向作物表型组大数据获取解析、作物种质资源表型鉴定等亟需高效率、智能化和低成本技术、装备及系统的问题,在系统梳理分析国内外农作物高通量表型平台相关技术产品研究现状的基础上,通过组织多学科的协同技术攻关,突破了作物表型组...面向作物表型组大数据获取解析、作物种质资源表型鉴定等亟需高效率、智能化和低成本技术、装备及系统的问题,在系统梳理分析国内外农作物高通量表型平台相关技术产品研究现状的基础上,通过组织多学科的协同技术攻关,突破了作物表型组大数据高通量获取和智能化解析中的关键技术难题,设计了具有自主知识产权的轻小敏捷型多传感器阵列、通用化成像单元和适用于多生境的固定式、移动式高通量表型平台装备,以及配套算法和软件平台,构建了农作物表型组大数据工厂成套技术装备体系。该体系由大田和设施作物高通量自主作业表型平台、室内器官和显微表型平台、大田和设施环境自动化种植管控设备、作物模型系统、数字孪生智慧管控平台和大数据计算服务中心等构成,可实现多生境、自动化、高通量、高效率、高精度的多源作物表型-环境数据协同采集,涵盖农作物群体、个体、器官和显微多重尺度,能够重建农林作物的三维形态结构并精准解析株型、产品、品质、抗性等表型组指标,是发展数字育种和智慧栽培的新一代信息化基础设施。农作物表型组大数据工厂技术装备体系创新了作物表型组大数据的产生、处理和服务模式,可为作物表型组理论技术的发展、基于AI for Science的平台化科研和工厂化的作物种质资源表型鉴定等提供体系化的技术装备支撑。展开更多
Mooring cable tension is a crucial parameter for evaluating the safety and reliability of a floating platform mooring system.The real-time mooring tension in an actual marine environment has always been essential data...Mooring cable tension is a crucial parameter for evaluating the safety and reliability of a floating platform mooring system.The real-time mooring tension in an actual marine environment has always been essential data that mooring system designers aim to acquire.To address the need for long-term continuous monitoring of mooring tension in deep-sea marine environments,this paper presents a mooring cable tension monitoring method based on the principle of direct mechanical measurement.The developed tension monitoring sensors were installed and applied in the mooring system of the"Yongle"scientific experimental platform.Over the course of one year,a substantial amount of in-situ tension monitoring data was obtained.Under wave heights of up to 1.24 m,the mooring tension on the floating platform reached 16.5 tons.Through frequency domain and time domain analysis,the spectral characteristics of mooring tension,including waveinduced force,slow drift force,and mooring cable elastic restoring force,were determined.The mooring cable elastic restoring force frequency was approximately half of that of the wave signal.Due to the characteristics of the hinge connection structure of the dual module floating platform,under some specific working conditions the wave-induced force was the maximum of the three different frequency forces,and restoring force was the smallest.展开更多
Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satell...Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satellite imagery and aerial data, remote sensing allows researchers to assess the health and extent of mangrove forests over large areas and time periods, providing insights into changes due to environmental stressors like climate change, urbanization, and deforestation. Coupled with web-based platforms, this technology facilitates real-time data sharing and collaborative research efforts among scientists, policymakers, and conservationists. Thus, there is a need to grow this research interest among experts working in this kind of ecosystem. The aim of this paper is to provide a comprehensive literature review on the effective role of remote sensing and web-based platform in monitoring mangrove ecosystem. The research paper utilized the thematic approach to extract specific information to use in the discussion which helped realize the efficiency of digital monitoring for the environment. Web-based platforms and remote sensing represent a powerful tool for environmental monitoring, particularly in the context of forest ecosystems. They facilitate the accessibility of vital data, promote collaboration among stakeholders, support evidence-based policymaking, and engage communities in conservation efforts. As experts confront the urgent challenges posed by climate change and environmental degradation, leveraging technology through web-based platforms is essential for fostering a sustainable future for the forests of the world.展开更多
The real-time screening of biomolecules and single cells in biochips is extremely important for disease prediction and diagnosis,cellular analysis,and life science research.Barcode biochip technology,which is integrat...The real-time screening of biomolecules and single cells in biochips is extremely important for disease prediction and diagnosis,cellular analysis,and life science research.Barcode biochip technology,which is integrated with microfluidics,typically comprises barcode array,sample loading,and reaction unit array chips.Here,we present a review of microfluidics barcode biochip analytical approaches for the high-throughput screening of biomolecules and single cells,including protein biomarkers,microRNA(miRNA),circulating tumor DNA(ctDNA),single-cell secreted proteins,single-cell exosomes,and cell interactions.We begin with an overview of current high-throughput detection and analysis approaches.Following this,we outline recent improvements in microfluidic devices for biomolecule and single-cell detection,highlighting the benefits and limitations of these devices.This paper focuses on the research and development of microfluidic barcode biochips,covering their self-assembly substrate materials and their specific applications with biomolecules and single cells.Looking forward,we explore the prospects and challenges of this technology,with the aim of contributing toward the use of microfluidic barcode detection biochips in medical diagnostics and therapies,and their large-scale commercialization.展开更多
In recent years,intensive human activities have increased the intensity of desertification,driving continual desertification process of peripheral meadows.To investigate the effects of restoration on soil microbial co...In recent years,intensive human activities have increased the intensity of desertification,driving continual desertification process of peripheral meadows.To investigate the effects of restoration on soil microbial communities,we analyzed vegetation-soil relationships in the Hulun Buir Sandy Land,northern China.Through the use of high-throughput sequencing,we examined the structure and diversity in the bacterial and fungal communities within the 0-20 cm soil layer after 9-15 a of restoration.Different slope positions were analyzed and spatial heterogeneity was assessed.The results showed progressive improvements in soil properties and vegetation with the increase of restoration duration,and the following order was as follows:bottom slope>middle slope>crest slope.During the restoration in the Hulun Buir Sandy Land,the bacterial communities were dominated by Proteobacteria,Actinobacteria,and Acidobacteria,whereas the fungal communities were dominated by Ascomycota and Basidiomycota.Eutrophic bacterial abundance increased with the restoration duration,whereas oligotrophic bacterial and fungal abundance levels decreased.The soil bacterial abundance significantly increased with the increasing restoration duration,whereas the fungal diversity decreased after 11 a of restoration,except that at the crest slope.Redundancy analysis showed that pH,soil moisture content,total nitrogen,and vegetation-related factors affected the bacterial community structure(45.43%of the total variance explained).Canonical correspondence analysis indicated that pH,total phosphorus,and vegetation-related factors shaped the bacterial community structure(31.82%of the total variance explained).Structural equation modeling highlighted greater bacterial responses(R^(2)=0.49-0.79)to changes in environmental factors than those of fungi(R^(2)=0.20-0.48).The soil bacterial community was driven mainly by pH,soil moisture content,electrical conductivity,plant coverage,and litter dry weight.The abundance and diversity of the soil fungal community were mainly driven by plant coverage,litter dry weight,and herbaceous aboveground biomass,while there was no significant correlation between the soil fungal community structure and environmental factors.These findings highlighted divergent microbial succession patterns and environmental sensitivities during sandy grassland restoration.展开更多
On 20 April 1994,China made its first o!cial full-function connection to the World Wide Web through a 64-kilobyte international leased line,marking the country’s formal entry into the global digital age.The year 2024...On 20 April 1994,China made its first o!cial full-function connection to the World Wide Web through a 64-kilobyte international leased line,marking the country’s formal entry into the global digital age.The year 2024 marked the 30th anniversary of the country’s entry into the internet era.展开更多
The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explore...The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explores the synergistic application of AI and HTE, highlighting their role in accelerating catalyst discovery, optimizing reaction parameters, and understanding structure-performance relationships. HTE facilitates the rapid preparation, characterization, and evaluation of diverse catalyst formulations, generating large datasets essential for AI model training. Machine learning algorithms, including regression models, neural networks, and active learning frameworks, analyze these datasets to uncover the underlying relationships between the data, predict performance, and optimize experimental workflows in real-time. Case studies across heterogeneous, homogeneous, and electrocatalysis demonstrate significant advancements, including improved reaction selectivity, enhanced material stability, and shorten discovery cycles. The integration of AI with HTE has significantly accelerated discovery cycles, enabling the optimization of catalyst formulations and reaction conditions. Despite these achievements, challenges remain, including reliance on researcher expertise, real-time adaptability, and the complexity of large-scale data analysis. Addressing these limitations through refined experimental protocols, standardized datasets, and interpretable AI models will unlock the full potential of AI-HTE integration.展开更多
Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.H...Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.However,the vast complexity of multicomponent systems poses a major challenge for identifying promising candidates through conventional experimental or computational methods.A high-throughput CALPHAD framework is developed to identify compositions with potential TWIP/TRIP behaviors in the Cr-Co-Ni and Cr-Co-Ni-Fe systems through systematic screening of stacking fault energy(SFE),FCC phase stability,and FCC-to-HCP transition temperatures(T0).The approach combines TC-Python automation with parallel Gibbs energy calculations across hundreds of thousands of compositions,enabling efficient extraction of metastable FCC-dominant alloys.The high-throughput results find 214 compositions with desired properties from 160,000 candidates.Detailed analysis of the Gibbs energy distributions,phase fraction trends,and temperature-dependent SFE evolution reveals critical insights into the thermodynamic landscape governing plasticity mechanisms in HEAs.The results show that only a narrow region of the compositional space satisfies all screening criteria,emphasizing the necessity of an integrated approach.The screened compositions and trends provide a foundation for targeted experimental validation.Furthermore,this work demonstrates a scalable,composition-resolved strategy for predicting deformation mechanisms in multicomponent alloys and offers a blueprint for integrating thermodynamic screening with mechanistic understanding in HEA design.展开更多
The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectrosc...The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectroscopy and first-principles calculations are time-and power-consuming,not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space.In the present work,an artificial intelligence ensemble comprising two classifiers(with F1 scores of 0.9125 and 0.925)and a regressor(with mean squared error of 0.0014 eV)is constructed to achieve high-precision prediction of the bandgap.The bandgap perovskite dataset is established through highthroughput prediction of bandgaps by the ensemble.Based on the self-built dataset,partial dependence analysis(PDA)is developed to interpret the bandgap influential mechanism.Meanwhile,an interpretable mathematical model with an R^(2)of 0.8417 is generated using the genetic programming symbolic regression(GPSR)technique.The constructed PDA maps agree well with the Shapley Additive exPlanations,the GPSR model,and experiment verification.Through PDA,we reveal the boundary effect,the bowing effect,and their evolution trends with key descriptors.展开更多
This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using s...This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using simulated pyrolysis data as the training dataset.Various feature extraction methods are utilized,and five neural network architectures are developed to predict the co-cracking product distribution based on molecular structures.High-throughput screening of 12946 molecules outside the training dataset identifies the top 10 initiators for each target product—ethylene,propylene,and butadiene.The relative error between predicted and simulated values is less than 7%.Additionally,reaction pathway analysis elucidates the mechanisms by which initiators influence the distribution of cracking products.The proposed framework provides a practical and efficient approach for the rapid identification and evaluation of high-performance cracking initiators.展开更多
基金the financial support of the National Natural Science Foundation of China(No.22168009)。
文摘Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single fluorescent probe(BDP-CHD)for high-throughput screening of phosgene,DCP and volatile acyl chlorides.The probe underwent a covalent cascade reaction with phosgene to form boron dipyrromethene(BODIPY)with bright green fluorescence.By contrast,DCP,diphosgene and acyl chlorides can covalently assembled with the probe,giving rise to strong blue fluorescence.The probe has demonstrated high-throughput detection capability,high sensitivity,fast response(within 3 s)and parts per trillion(ppt)level detection limit.Furthermore,a portable platform based on BDP-CHD was constructed,which has achieved high-throughput discrimination of 16 analytes through linear discriminant analysis(LDA).Moreover,a smartphone adaptable RGB recognition pattern was established for the quantitative detection of multi-analytes.Therefore,this portable fluorescence sensing platform can serve as a versatile tool for rapid and high-throughput detection of toxic phosgene,DCP and volatile acyl chlorides.The proposed“one for more”strategy simplifies multi-target discrimination procedures and holds great promise for various sensing applications.
基金the Beijing Academy of Agricultural and Forestry Sciences Phenotypic Collaborative Innovation Centre projects(grant no.KJCX201917)China's key R&D projects(grant no.2017YFD0201501)China National Natural Science Foundation Project(grant no.31871519).
文摘The lack of efficient crop phenotypic measurement methods has become a bottleneck in the field of breeding and precision cultivation.However,high-throughput and accurate phenotypic measurement could accelerate the breeding and improve the existing cultivation management technology.In view of this,this paper introduces a high-throughput crop phenotype measurement platform named the LQ-FieldPheno,which was developed by China National Agricultural Information Engineering Technology Research Centre.The proposed platform represents a mobile phenotypic high-throughput automatic acquisition system based on a field track platform,which introduces the Internet of Things(IoT)into agricultural breeding.The proposed platform uses the crop phenotype multisensor central imaging unit as a core and integrates different types of equipment,including an automatic control system,upward field track,intelligent navigation vehicle,and environmental sensors.Furthermore,it combines an RGB camera,a 6-band multispectral camera,a thermal infrared camera,a 3-dimensional laser radar,and a deep camera.Special software is developed to control motions and sensors and to design run lines.Using wireless sensor networks and mobile communication wireless networks of IoT,the proposed system can obtain phenotypic information about plants in their growth period with a high-throughput,automatic,and high time sequence.Moreover,the LQ-FieldPheno has the characteristics of multiple data acquisition,vital timeliness,remarkable expansibility,high-cost performance,and flexible customization.The LQ-FieldPheno has been operated in the 2020 maize growing season,and the collected point cloud data are used to estimate the maize plant height.Compared with the traditional crop phenotypic measurement technology,the LQ-FieldPheno has the advantage of continuously and synchronously obtaining multisource phenotypic data at different growth stages and extracting different plant parameters.The proposed platform could contribute to the research of crop phenotype,remote sensing,agronomy,and related disciplines.
基金supported by the National Key Research and Development Program(grant number:2022YFC2305304).
文摘Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing.Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing.Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing.Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.
基金funded by the National Natural Science Foundation of China(21904139)。
文摘Background:Tumor cell heterogeneity mediated drug resistance has been recognized as the stumbling block of cancer treatment.Elucidating the cytotoxicity of anticancer drugs at single-cell level in a high-throughput way is thus of great value for developing precision therapy.However,current techniques suffer from limitations in dynamically characterizing the responses of thousands of single cells or cell clones presented to multiple drug conditions.Methods:We developed a new microfluidics-based“SMART”platform that is Simple to operate,able to generate a Massive single-cell array and Multiplex drug concentrations,capable of keeping cells Alive,Retainable and Trackable in the microchambers.These features are achieved by integrating a Microfluidic chamber Array(4320 units)and a sixConcentration gradient generator(MAC),which enables highly efficient analysis of leukemia drug effects on single cells and cell clones in a high-throughput way.Results:A simple procedure produces 6 on-chip drug gradients to treat more than 3000 single cells or single-cell derived clones and thus allows an efficient and precise analysis of cell heterogeneity.The statistic results reveal that Imatinib(Ima)and Resveratrol(Res)combination treatment on single cells or clones is much more efficient than Ima or Res single drug treatment,indicated by the markedly reduced half maximal inhibitory concentration(IC50).Additionally,single-cell derived clones demonstrate a higher IC_(50) in each drug treatment compared to single cells.Moreover,primary cells isolated from two leukemia patients are also found with apparent heterogeneity upon drug treatment on MAC.Conclusions:This microfluidics-based“SMART”platform allows high-throughput single-cell capture and culture,dynamic drug-gradient treatment and cell response monitoring,which represents a new approach to efficiently investigate anticancer drug effects and should benefit drug discovery for leukemia and other cancers.
基金The research work was financially supported by the National Natural Science Foundation of China(Grant Nos.51979238 and 52301338)the Sichuan Science and Technology Program(Grant Nos.2023NSFSC1953 and 2023ZYD0140).
文摘Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate prediction of displacement and position of VIV in flexible risers remains challenging under actual marine conditions.This study presents a data-driven model for riser displacement prediction that corresponds to field conditions.Experimental data analysis reveals that the XGBoost algorithm predicts the maximum displacement and position with superior accuracy compared with Support vector regression(SVR),considering both computational efficiency and precision.Platform displacement in the Y-direction demonstrates a significant positive correlation with both axial depth and maximum displacement magnitude.The fourth point displacement exhibits the highest contribution to model prediction outcomes,showing a positive influence on maximum displacement while negatively affecting the axial depth of maximum displacement.Platform displacement in the X-and Y-directions exhibits competitive effects on both the riser’s maximum displacement and its axial depth.Through the implementation of XGBoost algorithm and SHapley Additive exPlanation(SHAP)analysis,the model effectively estimates the riser’s maximum displacement and its precise location.This data-driven approach achieves predictions using minimal,readily available data points,enhancing its practical field applications and demonstrating clear relevance to academic and professional communities.
基金financially supported by National Key R&D Program(2021YFF0701905)。
文摘In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.
基金financial support from the National Key Research and Development Program of China(2021YFB 3501501)the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011)+1 种基金Beijing Natural Science Foundation(No.Z230023)Beijing Science and Technology Commission(No.Z211100004321001).
文摘The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.
基金supported by the Portuguese Foundation for Science and Technology(Fundação para a Ciência e Tecnologia-FCT)it contributes to the Strategic Research Plan of the Centre for Marine Technology and Ocean Engineering(Grant No.UIDB/UIDP/00134/2020)funded the first author for his PhD Scholarship(Grant No.SFRH/BD/145602/2019).
文摘This paper presents an overview of the recent developments in hybrid wind-wave energy.With the focus on floating concepts,the possible configurations introduced in the literature are categorized and depicted,and the main conclusions obtained from the references are summarized.Moreover,offshore wind and wave resources are discussed in terms of complementarity and supplementarity,offering a new perspective to developing hybrid wind-wave energy systems that look for synergies not limited to maximizing power output.Then,the feasibility of the concepts under development is discussed in detail,with focus on technical feasibility,dynamic feasibility and limitations of the methods employed.The hybrid configurations that surpassed the experimental validation phase are highlighted,and the experimental results are summarized.By compiling more than 40 floating wind turbine concepts,new relations are drawn between power,wind turbine dimensions,platforms’draft and displacement,which are further related to the payload allowance of the units to accommodate wave devices and onboard power take-off systems.Bearing in mind that it is a challenge to model the exact dynamics of hybrid floating wind-wave platforms,this paper elucidates the current research gaps,limitations and future trends in the field.Lastly,based on the overview and topics discussed,several major conclusions are drawn concerning hybrid synergies,dynamics and hydrodynamics of hybrid platforms,feasibility of concepts,among other regards.
文摘Purpose–This study summarizes the overall situation of the resources of the national science and technology innovation platform in the railway industry,including the distribution of platform types,supporting institutions,construction sites,professional fields,etc.,to provide a reference for the further improvement and optimization of the national science and technology innovation platform system in the railway industry.Design/methodology/approach–Through literature review,field investigation,expert consultation and other methods,this paper systematically investigates and analyzes the development status of the national science and technology innovation platform in the railway industry.Findings–Taking the national science and technology innovation platform of the railway industry as the research object,this paper investigates and analyzes the construction,development and distribution of the national science and technology innovation platform of railway industry over the years.And the National Engineering Research Center of High-speed Railway and Urban Rail Transit System Technology was taken as an example to introduce its operation effect.Originality/value–China Railway has made great development achievements,with the construction and development of national science and technology innovation platform in the railway industry.In recent years,a large number of national science and technology innovation platforms have been built in the railway industry,which play an important role in railway technological innovation,standard setting and commodification,and Railway Sciences provide strong support for railway technology development.
基金2024 Anqing Normal University University-Level Key Project(ZK2024062D)。
文摘This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model follows a“three-stage”and“two-subject”framework,incorporating a structured design for teaching content and assessment methods before,during,and after class.Practical results indicate that this approach significantly enhances teaching effectiveness and improves students’learning autonomy.
文摘面向作物表型组大数据获取解析、作物种质资源表型鉴定等亟需高效率、智能化和低成本技术、装备及系统的问题,在系统梳理分析国内外农作物高通量表型平台相关技术产品研究现状的基础上,通过组织多学科的协同技术攻关,突破了作物表型组大数据高通量获取和智能化解析中的关键技术难题,设计了具有自主知识产权的轻小敏捷型多传感器阵列、通用化成像单元和适用于多生境的固定式、移动式高通量表型平台装备,以及配套算法和软件平台,构建了农作物表型组大数据工厂成套技术装备体系。该体系由大田和设施作物高通量自主作业表型平台、室内器官和显微表型平台、大田和设施环境自动化种植管控设备、作物模型系统、数字孪生智慧管控平台和大数据计算服务中心等构成,可实现多生境、自动化、高通量、高效率、高精度的多源作物表型-环境数据协同采集,涵盖农作物群体、个体、器官和显微多重尺度,能够重建农林作物的三维形态结构并精准解析株型、产品、品质、抗性等表型组指标,是发展数字育种和智慧栽培的新一代信息化基础设施。农作物表型组大数据工厂技术装备体系创新了作物表型组大数据的产生、处理和服务模式,可为作物表型组理论技术的发展、基于AI for Science的平台化科研和工厂化的作物种质资源表型鉴定等提供体系化的技术装备支撑。
文摘Mooring cable tension is a crucial parameter for evaluating the safety and reliability of a floating platform mooring system.The real-time mooring tension in an actual marine environment has always been essential data that mooring system designers aim to acquire.To address the need for long-term continuous monitoring of mooring tension in deep-sea marine environments,this paper presents a mooring cable tension monitoring method based on the principle of direct mechanical measurement.The developed tension monitoring sensors were installed and applied in the mooring system of the"Yongle"scientific experimental platform.Over the course of one year,a substantial amount of in-situ tension monitoring data was obtained.Under wave heights of up to 1.24 m,the mooring tension on the floating platform reached 16.5 tons.Through frequency domain and time domain analysis,the spectral characteristics of mooring tension,including waveinduced force,slow drift force,and mooring cable elastic restoring force,were determined.The mooring cable elastic restoring force frequency was approximately half of that of the wave signal.Due to the characteristics of the hinge connection structure of the dual module floating platform,under some specific working conditions the wave-induced force was the maximum of the three different frequency forces,and restoring force was the smallest.
文摘Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satellite imagery and aerial data, remote sensing allows researchers to assess the health and extent of mangrove forests over large areas and time periods, providing insights into changes due to environmental stressors like climate change, urbanization, and deforestation. Coupled with web-based platforms, this technology facilitates real-time data sharing and collaborative research efforts among scientists, policymakers, and conservationists. Thus, there is a need to grow this research interest among experts working in this kind of ecosystem. The aim of this paper is to provide a comprehensive literature review on the effective role of remote sensing and web-based platform in monitoring mangrove ecosystem. The research paper utilized the thematic approach to extract specific information to use in the discussion which helped realize the efficiency of digital monitoring for the environment. Web-based platforms and remote sensing represent a powerful tool for environmental monitoring, particularly in the context of forest ecosystems. They facilitate the accessibility of vital data, promote collaboration among stakeholders, support evidence-based policymaking, and engage communities in conservation efforts. As experts confront the urgent challenges posed by climate change and environmental degradation, leveraging technology through web-based platforms is essential for fostering a sustainable future for the forests of the world.
基金supported by the National Key Research and Development Plan of China(2023YFB3210400)the Natural Science Innovation Group Foundation of China(T2321004)+3 种基金the National Natural Science Foundation of China(62174101)Shandong University Integrated Research and Cultivation Project(2022JC001)Key Research and Development Plan of Shandong Province(Major Science and Technology Innovation Project2022CXGC020501).
文摘The real-time screening of biomolecules and single cells in biochips is extremely important for disease prediction and diagnosis,cellular analysis,and life science research.Barcode biochip technology,which is integrated with microfluidics,typically comprises barcode array,sample loading,and reaction unit array chips.Here,we present a review of microfluidics barcode biochip analytical approaches for the high-throughput screening of biomolecules and single cells,including protein biomarkers,microRNA(miRNA),circulating tumor DNA(ctDNA),single-cell secreted proteins,single-cell exosomes,and cell interactions.We begin with an overview of current high-throughput detection and analysis approaches.Following this,we outline recent improvements in microfluidic devices for biomolecule and single-cell detection,highlighting the benefits and limitations of these devices.This paper focuses on the research and development of microfluidic barcode biochips,covering their self-assembly substrate materials and their specific applications with biomolecules and single cells.Looking forward,we explore the prospects and challenges of this technology,with the aim of contributing toward the use of microfluidic barcode detection biochips in medical diagnostics and therapies,and their large-scale commercialization.
基金supported by the National Ecological Environment Survey and Assessment(2024-vertical-0107)the Fundamental Research Funds for the Central Public-interest Scientific Institution(2023YSKY-26)the Hulun Buir Grassland Ecological Restoration Comprehensive Survey Project(DD20230474).
文摘In recent years,intensive human activities have increased the intensity of desertification,driving continual desertification process of peripheral meadows.To investigate the effects of restoration on soil microbial communities,we analyzed vegetation-soil relationships in the Hulun Buir Sandy Land,northern China.Through the use of high-throughput sequencing,we examined the structure and diversity in the bacterial and fungal communities within the 0-20 cm soil layer after 9-15 a of restoration.Different slope positions were analyzed and spatial heterogeneity was assessed.The results showed progressive improvements in soil properties and vegetation with the increase of restoration duration,and the following order was as follows:bottom slope>middle slope>crest slope.During the restoration in the Hulun Buir Sandy Land,the bacterial communities were dominated by Proteobacteria,Actinobacteria,and Acidobacteria,whereas the fungal communities were dominated by Ascomycota and Basidiomycota.Eutrophic bacterial abundance increased with the restoration duration,whereas oligotrophic bacterial and fungal abundance levels decreased.The soil bacterial abundance significantly increased with the increasing restoration duration,whereas the fungal diversity decreased after 11 a of restoration,except that at the crest slope.Redundancy analysis showed that pH,soil moisture content,total nitrogen,and vegetation-related factors affected the bacterial community structure(45.43%of the total variance explained).Canonical correspondence analysis indicated that pH,total phosphorus,and vegetation-related factors shaped the bacterial community structure(31.82%of the total variance explained).Structural equation modeling highlighted greater bacterial responses(R^(2)=0.49-0.79)to changes in environmental factors than those of fungi(R^(2)=0.20-0.48).The soil bacterial community was driven mainly by pH,soil moisture content,electrical conductivity,plant coverage,and litter dry weight.The abundance and diversity of the soil fungal community were mainly driven by plant coverage,litter dry weight,and herbaceous aboveground biomass,while there was no significant correlation between the soil fungal community structure and environmental factors.These findings highlighted divergent microbial succession patterns and environmental sensitivities during sandy grassland restoration.
文摘On 20 April 1994,China made its first o!cial full-function connection to the World Wide Web through a 64-kilobyte international leased line,marking the country’s formal entry into the global digital age.The year 2024 marked the 30th anniversary of the country’s entry into the internet era.
基金supported by the Special Project of National Natural Science Foundation(42341204)the the National Natural Science Foundation of China(W2411009).
文摘The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explores the synergistic application of AI and HTE, highlighting their role in accelerating catalyst discovery, optimizing reaction parameters, and understanding structure-performance relationships. HTE facilitates the rapid preparation, characterization, and evaluation of diverse catalyst formulations, generating large datasets essential for AI model training. Machine learning algorithms, including regression models, neural networks, and active learning frameworks, analyze these datasets to uncover the underlying relationships between the data, predict performance, and optimize experimental workflows in real-time. Case studies across heterogeneous, homogeneous, and electrocatalysis demonstrate significant advancements, including improved reaction selectivity, enhanced material stability, and shorten discovery cycles. The integration of AI with HTE has significantly accelerated discovery cycles, enabling the optimization of catalyst formulations and reaction conditions. Despite these achievements, challenges remain, including reliance on researcher expertise, real-time adaptability, and the complexity of large-scale data analysis. Addressing these limitations through refined experimental protocols, standardized datasets, and interpretable AI models will unlock the full potential of AI-HTE integration.
基金supported by the U.S.Army Research Laboratory through their award#W911NF-22-2-0040the Ministry of Education,Youth and Sports of the Czech Republic through the e-INFRA CZ(ID:90254).
文摘Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.However,the vast complexity of multicomponent systems poses a major challenge for identifying promising candidates through conventional experimental or computational methods.A high-throughput CALPHAD framework is developed to identify compositions with potential TWIP/TRIP behaviors in the Cr-Co-Ni and Cr-Co-Ni-Fe systems through systematic screening of stacking fault energy(SFE),FCC phase stability,and FCC-to-HCP transition temperatures(T0).The approach combines TC-Python automation with parallel Gibbs energy calculations across hundreds of thousands of compositions,enabling efficient extraction of metastable FCC-dominant alloys.The high-throughput results find 214 compositions with desired properties from 160,000 candidates.Detailed analysis of the Gibbs energy distributions,phase fraction trends,and temperature-dependent SFE evolution reveals critical insights into the thermodynamic landscape governing plasticity mechanisms in HEAs.The results show that only a narrow region of the compositional space satisfies all screening criteria,emphasizing the necessity of an integrated approach.The screened compositions and trends provide a foundation for targeted experimental validation.Furthermore,this work demonstrates a scalable,composition-resolved strategy for predicting deformation mechanisms in multicomponent alloys and offers a blueprint for integrating thermodynamic screening with mechanistic understanding in HEA design.
基金supported by the National Research Foundation of Korea(NRF)funded by the Korean government(MSIT)(Grant number:RS-2025-02316700,and RS-2025-00522430)the China Scholarship Council Program。
文摘The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectroscopy and first-principles calculations are time-and power-consuming,not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space.In the present work,an artificial intelligence ensemble comprising two classifiers(with F1 scores of 0.9125 and 0.925)and a regressor(with mean squared error of 0.0014 eV)is constructed to achieve high-precision prediction of the bandgap.The bandgap perovskite dataset is established through highthroughput prediction of bandgaps by the ensemble.Based on the self-built dataset,partial dependence analysis(PDA)is developed to interpret the bandgap influential mechanism.Meanwhile,an interpretable mathematical model with an R^(2)of 0.8417 is generated using the genetic programming symbolic regression(GPSR)technique.The constructed PDA maps agree well with the Shapley Additive exPlanations,the GPSR model,and experiment verification.Through PDA,we reveal the boundary effect,the bowing effect,and their evolution trends with key descriptors.
基金The financial support provided by the Project of the National Natural Science Foundation of China (22308314,U22A20415)the Natural Science Foundation of Zhejiang Province (LQ24B060001)+1 种基金the "Pioneer" and "Leading Goose" Research & Development Program of Zhejiang (2022C01SA442617)the SINOPEC Technology Development Project (224244)
文摘This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using simulated pyrolysis data as the training dataset.Various feature extraction methods are utilized,and five neural network architectures are developed to predict the co-cracking product distribution based on molecular structures.High-throughput screening of 12946 molecules outside the training dataset identifies the top 10 initiators for each target product—ethylene,propylene,and butadiene.The relative error between predicted and simulated values is less than 7%.Additionally,reaction pathway analysis elucidates the mechanisms by which initiators influence the distribution of cracking products.The proposed framework provides a practical and efficient approach for the rapid identification and evaluation of high-performance cracking initiators.
