ACS Engineering Au最新文献

{"title":"Anchoring CFD Models to Real-World Bubble Columns Using Wall Pressure Fluctuations","authors":"Rupak Kumar,&nbsp;Vivek V. Ranade","doi":"10.1021/acsengineeringau.5c00090","DOIUrl":"10.1021/acsengineeringau.5c00090","url":null,"abstract":"<p>Bubble column reactors (BCRs) are widely used in chemicaland otherallied industries. Computational fluid dynamics (CFD) models are oftenused to simulate and optimize BCRs. However, these models still requiread hoc adjustments of model parameters to anchor simulations to real-worldreactors. A recently proposed idea suggests that wall pressure fluctuationscould be exploited to obtain relevant closure models. In this work,we evaluated this idea for simulating BCRs. A published case of abubble column with 0.1 m diameter and 2 m height, for which the gasholdup and wall pressure fluctuation data were available, was considered.Three-dimensional transient flow in the BCR was simulated by usingthe Euler–Euler models. In the first step, following the conventionalapproach, the values of the key parameter (<em>C</em>_D/<em>d</em>_B) for estimating interphasedrag were obtained by matching simulated results with experimentalgas holdup data. An artificial neural network (ANN) was then trainedto relate experimental wall pressure fluctuations data with thoseof (<em>C</em>_D/<em>d</em>_B).The hybrid model demonstrated high accuracy, with an <em>R</em>² of 0.99, and the predicted values fall within ±5%of the experimental data. The trained ANN was subsequently used toestimate the (<em>C</em>_D/<em>d</em>_B) for the cases unseen by the model. The wall pressure fluctuations-basedapproach successfully captured the influence of gas velocity on gasholdup for both the water and the 1.5% ethanol case. The approachwas also found to be reasonably successful for simulating the influenceof ethanol concentration on gas holdup. These findings demonstratethe feasibility of combining wall pressure fluctuations with machinelearning to estimate relevant model parameters. This work establishesa robust foundation for advancing the presented approach toward anchoringCFD models to real-world reactor systems.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"185–196"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147272082","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":"Modeling Solvatochromic Effect by a Two-Step Machine Learning Approach for Organic Chromophores","authors":"Zong-Rong Ye,&nbsp;Yu-Huan Huang,&nbsp;Berlin Chen,&nbsp;Ming-Kang Tsai","doi":"10.1021/acsengineeringau.5c00087","DOIUrl":"10.1021/acsengineeringau.5c00087","url":null,"abstract":"<p>Solvatochromism,also known as the environment-inducedmodulationof photophysical properties, plays a critical role in the design offluorescent probes, sensing systems, and optical materials. Traditionalmachine learning models often struggle to disentangle solute-intrinsiceffects from solvent-induced shifts, limiting both interpretabilityand predictive robustness. To address this challenge, we propose atwo-step regression framework that models fluorescence-related propertiesby first estimating a solute-only baseline, followed by a correctionstep capturing solvent contributions. Utilizing Morgan fingerprintsfor molecular representation and random forest regression for learning,the model was benchmarked on five comprehensive data sets encompassingwavelength of maximum adsorption and wavelength of maximum emission.Across all data sets, the two-step approach consistently outperformsconventional one-pot models in both accuracy and mean absolute error.For instance, the <em>Q</em>² and MAE of the testingset for predicting λ_emi of the Deep4Chem data setare 0.844 and 27.8 nm, respectively, using a two-step approach, outperformingthe corresponding results at 0.767 and 33.1 nm, respectively, usinga one-pot model. Permutation importance analysis reveals a chemicallyintuitive rebalancing of feature influence: solute descriptors dominateprediction, while solvent effects are modeled as secondary perturbations.This modular design not only enhances predictive power but also alignsmore closely with physical insights. The framework offers an alternativedirection for chemically interpretable, data-driven modeling of thesolvatochromic behavior in molecular systems.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"159–169"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148666115","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":"Wood Adhesives: Smart Adhesion, Aging, and Sustainability Assessment","authors":"Saurabh Kumar Kardam,&nbsp;Rohan Kumar,&nbsp;Vandana Kumari Jha,&nbsp;Pradip K. Maji,&nbsp;Aman Verma,&nbsp;Chhavi Verma","doi":"10.1021/acsengineeringau.5c00077","DOIUrl":"10.1021/acsengineeringau.5c00077","url":null,"abstract":"<p>The growing demandfor eco-materials has triggered high interestin bioadhesives such as wood composite materials. Most of the presentadhesives are petroleum-based. It is notable, however, that fossilresources are finite and their manufacture can harm the environment.Renewable biomass, however, has plenty of potential for use in woodadhesives. This review centers on progress made in developing sustainable,bioderived adhesives mostly for wood products. Renewable resourcessuch as lignin, tannin, proteins, chitosan, and starch are exploredas promising substitutes for traditional nonrenewable-based adhesives.The review also includes various techniques used to strengthen thebonding performance of these materials, including chemical modifications,cross-linking approaches, hybrid formulations, and the incorporationof nanomaterials. In addition, the review highlights emerging directionsin the field, including advances in smart adhesion, a better understandingof the bonding chemistry of common biobased adhesives, and recentfindings on their aging behavior and life-cycle assessment performance.The review primarily concentrates on wood-derived adhesives whilealso incorporating recent research on other emerging biobased adhesivesystems that are currently gaining attention.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"20–47"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148672318","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":"Inkjet Printing and Hydrogen-Assisted Annealing of Mo1–xNbxS2 Alloy Films for Ultrawide-Range Flexible Temperature Sensors","authors":"Yilin Zhou,&nbsp;Kexin Wang,&nbsp;Yingzhe Li,&nbsp;Chenxin Yuan,&nbsp;Manzhang Xu,&nbsp;Lu Zheng,&nbsp;Xuewen Wang,&nbsp;Weiwei Li","doi":"10.1021/acsengineeringau.5c00065","DOIUrl":"10.1021/acsengineeringau.5c00065","url":null,"abstract":"<p>With the rapid advancementof Industry 4.0, aerospace,energy,and chemical engineering, temperature sensors are facing technicaldemands in high-temperature environments. Conventional temperaturesensing materials, limited by their performance under extreme conditions,struggle to meet the requirements of modern industries. To addressthis challenge, this study proposes a fabrication process combininginkjet printing with hydrogen-assisted thermal annealing, leveragingthe unique electronic structure and tunable physicochemical propertiesof molybdenum–niobium disulfide (Mo_{1–<em>x</em>}Nb_<em>x</em>S₂). This methodenables the controllable preparation of Mo_{1–<em>x</em>}Nb_<em>x</em>S₂ alloy thin films,where inkjet printing facilitates low-cost, patterned fabricationand hydrogen annealing modulates crystallinity and defect density.The Mo_{1–<em>x</em>}Nb_<em>x</em>S₂-based flexible temperature sensor exhibits outstandingsensing performance, including a reliable temperature response acrossa broad range (10–1000 K) and exceptional stability after a1000 cyclic thermal test. This work not only provides a promisingsolution for temperature monitoring in extreme environments but alsoopens new avenues for the development of next-generation flexibleelectronics.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"105–114"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147894032","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":"Computationally Efficient LSTM–Euler Hybrid Model Framework: Application to Industrial-Scale Fermentation with Experimental Validation","authors":"Aahil Khambhawala,&nbsp;Parth Shah,&nbsp;Satchit Nagpal,&nbsp;Jong-Hwan Shin,&nbsp;Jae Hoon Lee,&nbsp;Joseph Sang-Il Kwon","doi":"10.1021/acsengineeringau.5c00074","DOIUrl":"10.1021/acsengineeringau.5c00074","url":null,"abstract":"<p>Industrial-scale fermentation processes are subject tosignificantvariability due to fluctuating raw material quality, seed cultureperformance, and environmental disturbances, which challenge the assumptionsof static-parameter first-principles models. This study introducesa hybrid modeling framework that integrates a first-principles fermentationmodel with a long short-term memory (LSTM) neural network to dynamicallyestimate kinetic parameters in real time. The LSTM receives a five-time-steprolling window of virtual or experimental measurements comprisingbiomass, substrates, product, dissolved oxygen, and feed flow ratesand outputs time-varying estimates of kinetic parameters, such asgrowth rates, yield coefficients, and inhibition constants. Theseparameters are then input into the first-principles model, which evolvesthe system states using Euler’s method. Comparative analysisagainst two baseline models (i.e., a physics-integrated feedforwardneural network and an LSTM–ODEint model) demonstrated superiorpredictive accuracy and computational tractability for the developedmodel. Temporal error and runtime analyses confirmed that the proposedLSTM–Euler architecture achieves faster convergence, smoothertraining, and lower loss in comparison to traditional neural network(NN)-based hybrid models. The LSTM component captures batch-to-batchvariations that conventional hybrid models miss, while the fixed-stepEuler solver avoids the instability introduced by adaptive solvers,leading to stable gradient flows and consistent parameter updates.This combination enables higher predictive accuracy with reduced computationaloverhead, offering performance gains over existing hybrid modelingimplementations.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"115–125"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148650911","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":"Photocurable Adhesive Composed of Modified Castor Oil and Enhanced by Valorized Glycerol from Waste Cooking Oil","authors":"Vojtěch Jašek,&nbsp;Otakar Bartoš,&nbsp;Jan Prokeš,&nbsp;Eliška Kameníková,&nbsp;Radek Přikryl,&nbsp;Silvestr Figalla","doi":"10.1021/acsengineeringau.5c00084","DOIUrl":"10.1021/acsengineeringau.5c00084","url":null,"abstract":"<p>This article describes the production of a photocurableadhesivefrom renewable sources, i.e., castor oil and waste cooking oil. Aninnovative castor oil ethanolamide methacrylate (Oil-EA-Met) was synthesizedto form the primary binder precursor. The waste-oil-resourced glycerolderivatives, modified to mono-, di-, and trimethacrylate derivatives(MGLY, DGLY, and TGLY), represent functional additives. All productswere cross-analyzed using ¹H NMR, ESI-MS, FTIR, and GC-FID.Oil-EA-Met represented 75% of the formulated precursor systems, whilethe functional additive (MGLY, DGLY, TGLY, and a commercially available2-hydroxyethyl methacrylate (HEMA)) represented 25% of the curableprecursor. MGLY had the best impact on the adhesion performance. Thisadditive exhibited the highest polymerization reactivity (<em>E</em>_a = 68.2 kJ/mol), a complete miscibility withwater, the highest thermal resistivity, a thermomechanical profile,a flexural character, and the highest ISO 2049 adhesion levels onwood–glass substrates (reaching level 5 (the highest)) andwood–PMMA substrates (level 3). The adhesive containing MGLYreached a similar wood–glass adhesion shear strength of 1400kPa as the system with HEMA and surpassed the HEMA system for thewood–PMMA system (the MGLY system reached 800 MPa; the HEMAsystem achieved 530 MPa).</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"142–158"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148624858","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":"Machine Learning for Catalytic Reaction Systems: A Framework for Complex Chemical Processes","authors":"M. Erdem Günay,&nbsp;Ramazan Yıldırım","doi":"10.1021/acsengineeringau.5c00093","DOIUrl":"10.1021/acsengineeringau.5c00093","url":null,"abstract":"<p>Machine learning (ML) and artificial intelligence (AI)have beenrecognized as transformative tools in chemical engineering, offeringopportunities for accelerated discovery, design, and optimization.In this work, the field of catalysis is selected as a case study todemonstrate how AI/ML can be employed to complement traditional experimentaland computational approaches. The integration of ML across the fullscope of catalytic reaction systems is investigated from initial catalystscreening and material design to reactor development, process monitoring,and real-time control. Co-occurrence analysis for catalysis and MLwas performed using 12,743 author keywords from 3924 papers, whilesmaller subsets for author keywords were used to analyze the co-occurrenceof ML for specific aspects of catalysis; the number of papers usedto review and analyze the basic trends and findings was 174, as givenin references. Applications of ML in analyzing catalytic performance,characterizing structures through spectroscopic data, developing kineticand mechanistic models, and addressing transport limitations are highlighted.Emerging strategies such as physics-informed ML, hybrid frameworks,and generative AI are regarded as particularly promising for overcomingdata scarcity, interpretability, and scalability challenges. A comprehensiveview of the role of ML in catalysis is presented by tracing the evolutionof the field and identifying future opportunities. Consequently, aroadmap is suggested for applying similar approaches to other complexchemical engineering processes.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"48–67"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148666280","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":"Subcritical Water Hydrolysis-Derived Hydrochar from Grape Pomace (Vitis vinifera L.): A Novel Adsorbent for Removal of Fermentation Inhibitors","authors":"Luiz E. N. Castro,&nbsp;Manoel V. F. Barrionuevo,&nbsp;Gabriel C. Bevilaqua,&nbsp;Tiago L. C. T. Barroso,&nbsp;Vanessa C. Ferreira,&nbsp;José M. S. S. Esperança,&nbsp;Mauricio A. Rostagno,&nbsp;Isabel A. A. C. Esteves,&nbsp;Tania Forster-Carneiro","doi":"10.1021/acsengineeringau.5c00095","DOIUrl":"10.1021/acsengineeringau.5c00095","url":null,"abstract":"<p>This study exploresthe production of adsorbents from grape pomace(<em>Vitis vinifera</em> L.) via subcriticalwater hydrolysis (SWH) and their application as sustainable materialsfor removing fermentation inhibitors. Grape pomace, an abundant lignocellulosicbyproduct of the wine industry, underwent SWH to yield a hydrolysisresidue, which was subsequently chemically and thermally modifiedto generate various adsorbents (C2–C4). Among these, the C4material, activated with phosphoric acid and treated under autoclaveconditions, exhibited the most favorable properties, including a highsurface area (777.2 m² g^–1), microporosity,surface acidity, and negative surface charge. Batch and fixed-bedadsorption experiments confirmed C4’s superior capacity toselectively adsorb inhibitory compounds such as acetic acid, 5-hydroxymethylfurfural(5-HMF), and furfural, while preserving fermentable sugars. The adsorptionmechanisms were comprehensively analyzed using kinetic, equilibrium,and thermodynamic models. Adsorption was determined to be spontaneousand endothermic, primarily governed by physisorption, as evidencedby low activation energies and favorable thermodynamic parameters.Regeneration studies demonstrated that C4 could be effectively reusedfor over five cycles with minimal loss of adsorption capacity, particularlyfor furfural and 5-HMF, using alkaline water as the eluent. The practicalutility of C4 was validated through fermentability tests, which demonstratedsignificantly improved performance in detoxified hydrolysates, includinga 3.2-fold increase in ethanol production compared to nondetoxifiedsamples, as well as xylitol production. Overall, this study highlightsthe promise of subcritical hydrochar produced from water as an affordable,effective, and eco-friendly adsorbent suitable for biorefinery processes.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"6 1","pages":"197–221"},"PeriodicalIF":9.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148656207","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":"Holistic, Literature-Informed Critical Mineral Life Cycle Assessment Guidelines: An Essential Foundation for the Energy Transition.","authors":"Jenna N Trost, Jennifer B Dunn, Kimberly R Marion Suiseeya","doi":"10.1021/acsengineeringau.5c00075","DOIUrl":"10.1021/acsengineeringau.5c00075","url":null,"abstract":"<p><p>In this paper, we demonstrate that life cycle assessment (LCA) is a valuable tool for evaluating the trade-offs between critical mineral acquisition and its resulting environmental impacts, but the applications of LCA to critical mineral mining are inconsistent and limited. These inconsistencies inhibit effective comparison of mines' effects and decision-making in support of environmentally responsible mineral supply chains. To illustrate these limitations, we analyzed how 74 peer-reviewed and gray literature critical mineral mining LCAs applied the four phases of LCA. To further assess how these LCAs account for environmental impacts, we created a data set of critical mining impacts reported in the EJ Atlas. Based on this thorough assessment, we propose a series of guidelines for each LCA phase for application to critical mineral mining. These recommendations provide an opportunity to standardize critical mineral mining LCAs and enable better comparison to inform decision-making and mining policy development.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 6","pages":"621-638"},"PeriodicalIF":5.1,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12715796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145805899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optical Coherence Tomography Velocimetry for In-Line Processing: The Spherical-to-Wormlike Micelle Transition.","authors":"Owen Watts Moore, Thomas Andrew Waigh, Philip Martin, Cesar Mendoza, Harvey Brimelow, John Naughton, Adam Kowalski","doi":"10.1021/acsengineeringau.5c00045","DOIUrl":"10.1021/acsengineeringau.5c00045","url":null,"abstract":"<p><p>Personal care products are often dynamically formulated <i>in situ</i>. Variations in the chemistry of the base components (e.g., their polydispersities or ionic contents) require extensive off-line rheological analysis to ensure the products meet benchmarks for performance and thus consumer satisfaction. An in-line alternative for rheological quality control and monitoring thus has the potential to improve efficiency on industrial pipelines. Therefore, we demonstrate optical coherence tomography velocimetry (OCT-V) for the in-line processing of a shampoo based on SLES and CAPB with varying concentrations of salt. OCT-V is a noninvasive quasi-elastic light scattering technique, capable of spatially resolved velocity measurements with an axial depth resolution of 9 μm and a penetration depth of 1.5 mm into the samples. Our in-line apparatus uses infrared light (the wavelength is 1315 nm) and has been optimized for live manufacturing with a 200 L test rig at the Unilever R<i>&</i>D lab. The addition of salt to the shampoo is used for <i>in situ</i> formulation to increase the viscosity, inducing the spherical-to-wormlike micelle transition. To create in-line rheological benchmarks, we measured time averaged velocity profiles and transient velocity fluctuations in the shampoo formulations at imposed flow rates of <i>Q</i> = 500 and 1000 L/h. For a shampoo formulation with 1.1<i>%</i> NaCl salt, fits of the Hagen-Poiseuille equation to velocity profile data give flow rates that are in close agreement with the imposed values, <i>Q</i> _<i>HP</i> = 502 ± 2 L/h and 944 ± 4 L/h, corresponding to a 0.4<i>%</i> and 5.6<i>%</i> error, respectively. Combined with measurement of the longitudinal pressure drop, this could be used to calculate constitutive properties, such as the viscosity. The standard deviation of the distribution of transient velocity fluctuations is a decreasing function of salt concentration due to the increasing viscosity. If calibrated to the desired end product, the velocity fluctuations could also be used as a reproducible indicator of product quality on industrial pipelines.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 6","pages":"639-647"},"PeriodicalIF":5.1,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12715783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145805881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}