ACS Engineering Au最新文献

{"title":"Physically Consistent Neural Network Surrogates of Catalytic Reactors with Detailed Surface Kinetics","authors":"Felix A. Döppel,&nbsp;Mauro Bracconi,&nbsp;Matteo Maestri","doi":"10.1021/acsengineeringau.5c00106","DOIUrl":"10.1021/acsengineeringau.5c00106","url":null,"abstract":"<p>Motivated by their potential to unlock real-time controlof catalyticdevices and model-based process design under full mechanistic detail,we developed physics-informed neural networks (PINNs) that considerdetailed surface kinetics through an auxiliary kinetic surrogate modeland enforce atom conservation as a hard constraint. In particular,we inform the PINNs about detailed surface kinetics by coupling withan auxiliary neural network that determines the surface reaction rates.This allows meshless predictions of composition profiles and removesthe most time-consuming step from model training. Further, we proposea dedicated neural network layer that imposes atom conservation asa hard constraint. This guarantees the physical consistency of predictedreactor compositions and thus reduces the amount of data requiredfor model training, in our case, by a factor of 10. We demonstratethat our new framework provides accurate and consistent concentrationprofiles of a CO₂ methanation reactor more than 1000 timesfaster than established direct integration schemes.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 2","pages":"359–371"},"PeriodicalIF":9.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148624331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Physics-Based Reduced-Order Model to Predict Gas Holdup in Bubble Columns","authors":"Vamsi Pyla,&nbsp;Racha Varun Kumar,&nbsp;Varun Shastry,&nbsp;Aditya Singh,&nbsp;Himanshu Goyal","doi":"10.1021/acsengineeringau.5c00101","DOIUrl":"10.1021/acsengineeringau.5c00101","url":null,"abstract":"<p>Bubble columns are extensively used in the process industryforgas–liquid contact operations. The Euler–Euler CFD modelis commonly used to simulate bubble columns. However, the high computationalcost of these simulations makes them impractical for rapid design-spaceexploration. Consequently, bubble column design relies heavily onempirical correlations, which have limited generalizability and predictiveaccuracy. To address this limitation, a physics-based reduced-ordermodel (ROM) is developed via cross-sectional averaging of the Euler–Eulergas and liquid phase continuity and momentum conservation equations.The developed ROM consists of five algebraic equations without anytuning parameters. The unclosed terms appearing in the ROM are analyzedand closed using 3D Euler–Euler CFD simulations of bubble columnexperiments. The ROM can predict the overall gas holdup and the cross-sectionallyaveraged gas and liquid velocities within seconds. The model’saccuracy is evaluated against several experiments from the literature,covering a wide range of designs (e.g., column diameter, aspect ratio,and orifice size) and inlet gas velocity (0.01 to 0.2 m/s). The developedROM outperforms existing empirical correlations in predicting thegas holdup for Weber number <em>We</em> &lt; 2.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 2","pages":"345–358"},"PeriodicalIF":9.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148666341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supercritical CO2‑Assisted Solvent Atomization through a Two-Fluid Nozzle: CFD Modeling and Experimental Characterization of the Near-Field Region","authors":"Mohamad Baassiri,&nbsp;Vivek Ranade,&nbsp;Luis Padrela","doi":"10.1021/acsengineeringau.5c00086","DOIUrl":"10.1021/acsengineeringau.5c00086","url":null,"abstract":"<p>Supercritical CO₂ (scCO₂)-assistedspraydrying can be adapted for continuous drug particle production; however,limited research has been reported on modeling its performance whilefocusing on the trans-critical operation of the employed nozzles.In this context, we closely explore the behavior of the scCO₂-assisted atomization process of methanol, an organic solvent widelyemployed for spray drying of Active Pharmaceutical Ingredients (APIs).To this end, an Eulerian–Lagrangian Computational Fluid Dynamics(CFD) framework using Ansys FLUENT (2024R2) was developed to describethe scCO₂-assisted atomization of methanol through a micro-orificetwo-fluid nozzle structure, while thermodynamic submodels were integratedvia user-defined functions. Experimental validation and calibrationwithin a set of tested conditions were carried out with the aid ofreal-time laser-diffraction-based droplet/particle sizing methods.Simulations revealed the formation of methanol microdroplets (2–5μm) in the near-field, a prerequisite for nanoparticle formationfurther downstream of the nozzle. Semiempirical correlations applicablefor the demonstrated range of conditions were proposed for cost-effectiveprocess modeling, and guidelines on best practices were outlined.Experimental characterization of the produced droplets matched predictionswith an average error not exceeding 10%. Future work will build onthe developed methods to simulate atomization of an scCO₂–methanol–API solution to offer a cost-effective optimizationtool for relevant drug particle production setups.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 2","pages":"307–321"},"PeriodicalIF":9.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148656253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graphene Oxide Membranes for Sustainable Recycling: Poly(styrene) Fractionation by Organic Solvent Nanofiltration","authors":"Natechanok Yutthasaksunthorn,&nbsp;Yuchen Chang,&nbsp;Van Son Nguyen,&nbsp;Kaung Su Khin Zaw,&nbsp;Scott A. Sinquefield,&nbsp;Carsten Sievers,&nbsp;Sankar Nair","doi":"10.1021/acsengineeringau.5c00102","DOIUrl":"10.1021/acsengineeringau.5c00102","url":null,"abstract":"<p>Efficient separationand purification of polymeric mixturesisan important challenge in plastic recycling. Here we demonstrate arobust graphene oxide (GO) membrane platform capable of separatinglow- and high-molecular-weight poly­(styrene) (PS) in nonpolar solvents.By tuning GO membrane properties through pillaring with a polyconjugatedaromatic compound (PAC) and controlled reduction, we obtain the efficientnanofiltration of poly­(styrene) in a hydrocarbon solvent, enablingthe removal of monomers and low-molecular-weight oligomers. Over 600h of continuous operation, the pillared membrane maintains a stablehigh flux of 8 ± 1 L m^–2 h^–1 and total rejection of high-MW polymer. Postfractionation,the enriched high-MW retentate has a 2-fold higher yield of styrenemonomers in mechanocatalytic ball-milling depolymerization comparedto unfractionated PS. Removing oligomeric diluents improves energytransfer, suppresses chain transfer, and promotes chain scission followedby chain-end depropagation. Thus, fractionation by organic solventnanofiltration with GO membranes can enable scalable and efficientroutes to mechanochemical polymer recycling.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"82–89"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147272117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Driven Deployable Water Quality Management: Benchmarking Effluent BOD Prediction at a European Pulp and Paper Mill","authors":"Fatemeh Naeijian,&nbsp;Ali Arefinia,&nbsp;Mohammed Tamim Zaki,&nbsp;Kevin D. Orner,&nbsp;Mohammadsoroush Tafazzoli,&nbsp;Lewis S. Rowles","doi":"10.1021/acsengineeringau.5c00088","DOIUrl":"10.1021/acsengineeringau.5c00088","url":null,"abstract":"<p>The pulp and paper industry consumes 10–300 m³ of water per ton of product and generates substantial wastewatervolumes. These facilities collect extensive operational data thatremain underutilized for treatment optimization. However, systematiclong-horizon comparisons of multiple machine learning algorithms foreffluent prediction under full-scale operating conditions remain scarce.Using 1,033 daily records from a full-scale European mill (January2020–October 2022), we evaluated five machine learning modelsto predict effluent biochemical oxygen demand (BOD) for environmentalcompliance monitoring. Input variables included flow rate, influenttotal suspended solids (TSS), influent BOD, temperature, aerationrate, hydraulic retention time, and chemical dosing. We benchmarkedsupport vector machines (SVMs), artificial neural networks (ANNs),genetic programming (GP), decision trees (DTs), and random forests(RFs). SVM achieved the best performance (R² = 0.85, NSE= 0.82, RMSE = 5.5 mg/L), while ANN delivered competitive accuracy(R² = 0.82, RMSE = 6.8 mg/L) with the fastest runtime.GP produced interpretable mathematical equations, and DT/RF providedhigh operational transparency despite lower accuracy. These modelscan inform operational decisions related to aeration control, chemicaldosing, and compliance management. By combining routinely monitoredprocess variables with laboratory-measured influent BOD, our modelsdemonstrated strong retrospective predictive performance across athree-year data set. This work establishes a benchmark for pulp andpaper wastewater management and clarifies pathways for integratingreal-time sensors to enable predictive process control.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"170–184"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148624495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive Chemical Kinetic Modeling: Where We Succeed, Where We Struggle, and What Comes Next","authors":"Alon Grinberg Dana,&nbsp;Kevin M. Van Geem,&nbsp;Carlo Cavallotti,&nbsp;William H. Green","doi":"10.1021/acsengineeringau.5c00091","DOIUrl":"10.1021/acsengineeringau.5c00091","url":null,"abstract":"<p>Chemical kinetic modeling plays a foundational role infields rangingfrom energy to environmental science, pharmaceuticals, and advancedmaterials. The past two decades have seen remarkable progress, particularlyin modeling gas-phase reactions for thermochemical processes, leadingto impactful industrial applications such as steam cracking and airquality management. However, new challenges are emerging. The successfuldevelopment of systematic methodologies for the description of gas-phasekinetics opens the possibility to apply the same approach to the studyof more challenging systems. Here, we review recent advances, includingab initio transition state theory-based master equation estimationof elementary rates, automated mechanism generation, machine-learning-assistedkinetics, and uncertainty quantification, and discuss the advancesneeded to apply the same methodological approach in areas such asheterogeneous catalysis, electrochemistry, liquid-phase and solid-statereactivity, and multiscale model integration. We advocate for thedevelopment of targeted tools, especially methods that go beyond empiricaltuning toward first-principles-based predictions. We highlight theneed for accessible software and AI-augmented workflows to democratizemodeling for industry and academia alike. In this perspective, wecall attention to not only what has worked but also what remains unsolved,advocating to avoid overemphasizing successes in scientific worksat the expense of realism. The next decade should focus on predictivecapability, physical accuracy, and community infrastructure (e.g.,databases and services) to enable innovation across diverse fields.We argue that kinetic modeling, properly equipped, can acceleratediscovery far beyond its traditional domains.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"1–19"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147272098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Persulfate Infiltration in Degrading Soil Residual Trichloroethylene in the Vadose Zone – A One-Dimensional Column Study","authors":"Chenju Liang,&nbsp;Jia-He Shi","doi":"10.1021/acsengineeringau.5c00094","DOIUrl":"10.1021/acsengineeringau.5c00094","url":null,"abstract":"<p>Trichloroethylene (TCE) contamination commonly occursin both soiland groundwater. Because regulatory standards differ between thesemedia, soil may meet compliance levels, while residual TCE remainsin the unsaturated zone. Such residues can later diffuse into groundwaterduring rainfall infiltration or water table fluctuations, resultingin secondary contamination. This study integrated porous pipe infiltrationwith in situ chemical oxidation to assess whether adjusting infiltrationcycles and retention times can effectively deliver sodium persulfate(Na₂S₂O₈, SPS) into unsaturated soilsvia matrix flow. One-dimensional soil column experiments were conductedto observe SPS distribution over time and evaluate soil TCE residuesremoval. The research proceeded in two phases: (a) examining the SPSdistribution under varying infiltration cycles and (b) assessing theTCE removal efficiency. A longer SPS residence time, achieved witha pulse/no-pulse interval (<em>t</em>_p/<em>t</em>_np) = 5/25 (min) over three cycles, producedmore uniform oxidant distribution. Subsequent tests compared SPS retentiontimes (24–120 h) to evaluate their effect on residual TCE degradation.Soil TCE removal ranged from 82 to 97% depending on SPS retentiontime. In the 120 h retention test, a 20 h water flushing of the treatedsoil column demonstrated TCE removal, achieving 99.9% efficiency,with effluent TCE concentrations of 0.05–0.45 mg/L, versusup to 11.8 mg/L in untreated controls. Overall, results demonstratedthat under fixed <em>t</em>_p, increasing <em>t</em>_np enhances matrix potential, improving liquidtransport and sustaining moisture at depth. This study shows thatcontrolled oxidant infiltration is a promising remediation approachfor addressing TCE contamination in unsaturated zones.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"222–230"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148656249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthesis of Amine-Loaded Styrene–Divinylbenzene Copolymers for Carbon Dioxide Capture","authors":"Smita S. Nande,&nbsp;Baijayantimala Garnaik","doi":"10.1021/acsengineeringau.5c00082","DOIUrl":"10.1021/acsengineeringau.5c00082","url":null,"abstract":"<p>Carbon dioxide capture by various tertiary amines andquaternarybulky ammonium salts loaded in cross-link hybrid polymer (styrene–divinylbenzenecopolymer) was explored. These polymeric materials were prepared bysuspension polymerization in one pot using monomers like styrene anddivinylbenzene in the presence of various amines to obtain uniformspherical beads. The homopolymer was separated from the cross-linkhybrid product by Soxhlet extraction. The synthetic strategy is cost-effectiveand user-friendly, can be easily scaled up for production, and confirmsbetter mechanical strength. The amine-loaded microporous polymericspheres displayed a microsphere size of 5.2 nm with a high surfacearea of ∼25–310 m² g^–1.These hydrophobic polymeric spherical beads were thermally stableup to 200 °C. The quaternary ammonium group salt (bulky)-loadedbeads presented high carbon dioxide uptake (up to ∼16.4 wt% at 273 K /1.0 bar) due to the presence of lone pair electrons ofthe heteroatom, which might improve the interaction between the polymericmatrices and CO₂ molecules through local-dipole/quadrupleinteractions. In the case of tertiary amines, a high uptake of carbondioxide (up to ∼15.2 wt % at 273 K /1.0 bar) was achieved,which is attributed to the formation of bicarbonate, as there maybe the possibility of the presence of little moisture in the system,which facilitates the interaction. These hybrid polymeric materialsexhibited a better storage capacity of CO₂.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"126–141"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148672569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermochemical Conversion of Apple Pomace into Engineered Biochar: Production, Properties, and Technological Applications","authors":"Josiel Martins Costa,&nbsp;Tânia Forster-Carneiro","doi":"10.1021/acsengineeringau.5c00107","DOIUrl":"10.1021/acsengineeringau.5c00107","url":null,"abstract":"<p>The engineering ofcarbonaceous materials from agri-food residueshas become a promising strategy to advance sustainable materials,energy technologies, and circular waste management. Apple pomace,a high-volume byproduct of the juice industry, is an attractive feedstockfor biochar because of its high carbohydrate content and abundanceof phenolic compounds. In this way, this perspective provides a comprehensiveengineering analysis of apple pomace-derived biochar, focusing onhow feedstock characteristics, thermochemical conversion pathways,and reactor configurations govern the resulting structural, physicochemical,and functional properties. We synthesize recent advances in adsorption,catalysis, soil conditioning, energy storage, and electrochemicalapplications, emphasizing structure–property–performancerelationships relevant to material design. Comparative analysis revealsthat pyrolysis temperature is the primary driver of surface area,aromaticity, and functional group distribution, with high-temperaturebiochar favoring electrochemical and catalytic applications, whilelow-to-intermediate temperatures enhance adsorption and soil-relatedfunctionalities. Apple pomace–derived biochar exhibits competitiveperformance relative to other agri-food byproducts, particularly inadsorption capacity and electrochemical activity, due to its oxygen-richsurface chemistry and feedstock-specific mineral composition. Keylimitations, including variable feedstock quality, limited recyclabilityassessments, incomplete technoeconomic evaluations, and the lack ofstandardized characterization protocols, are identified as constraintsto scale-up and industrial deployment. Addressing these gaps is essentialto position apple pomace biochar as an engineered material with thepotential for integration into sustainable processes, low-carbon technologies,and circular bioeconomy frameworks.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"68–81"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148650797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intrinsic Kinetics of Polyethylene Terephthalate Pyrolysis via Micropyrolysis and Multivariate Chromatographic Analysis","authors":"Barbara Alejandra Perez,&nbsp;Hilal Ezgi Toraman","doi":"10.1021/acsengineeringau.5c00055","DOIUrl":"10.1021/acsengineeringau.5c00055","url":null,"abstract":"<p>This study provides an in-depth investigation of theprimary decompositionof polyethylene terephthalate (PET) via pyrolysis, employing an experimental-analyticworkflow that integrates design of experiments (DoE), micropyrolysiscoupled with comprehensive two-dimensional gas chromatography (GC×GC),and multivariate data analysis to verify intrinsic kinetic conditionsand elucidate evolving product distributions for mapping key reactionpathways. Peaks that could not be identified using commercial spectrallibraries were assigned using Mass Frontier simulations, enablingthe identification of divinyl terephthalate, ethyl vinyl terephthalate,and 2-(benzoyloxy)­ethyl vinyl terephthalate. A polar×polar (non-orthogonal)column set tailored for the detection of carboxylic acids enhancedthe quantification of benzoic acid, 4-vinylbenzoic acid, 4-ethylbenzoicacid, and methylbenzoic acid by up to 6-fold relative to an orthogonalcolumn combination (non-polar×mid-polar). Moreover, pyrolysisvariables were systematically evaluated using a Box-Behnken design(BBD), encompassing pyrolysis temperature (500–600 °C),sample weight (50–150 μg), and carrier gas flow rate(100–300 mL min^–1). Among these, pyrolysistemperature was the only statistically significant factor influencingproduct yields, ranging from 58.78 to 84.26 wt %. In contrast, neitherthe sample weight nor the carrier gas flow rate had a significanteffect on product yields within the evaluated experimental space.At 600 °C, the major pyrolysis products were benzoic acid (upto 20.20 ± 1.46 wt %) and CO₂ (up to 21.28 ±1.46 wt %), which can be produced through decarboxylation reactions.These findings underscore the critical importance of selecting appropriateanalytical columns for the accurate quantification of heteroatom-containingproducts such as carboxylic acids, which may otherwise be underestimatedor undetected due to their reactivity with the stationary phase ofnon-polar and mid-polar columns, as well as other GC components. Theyalso highlight the importance of selecting pyrolysis conditions forinvestigating the primary decomposition of PET under an isothermalkinetically limited regime.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"90–104"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147272107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}