{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T15:57:26Z","timestamp":1791302246158,"version":"4.1.0"},"reference-count":65,"publisher":"American Chemical Society (ACS)","issue":"39","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003093","name":"Ministry of Higher Education, Malaysia","doi-asserted-by":"publisher","award":["203\/PTEKIND\/6711702"],"award-info":[{"award-number":["203\/PTEKIND\/6711702"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Kapok is a lightweight hollow tropical fiber with potential as a renewable feedstock, but its waxy surface and lignin-containing matrix complicate aqueous processing. This study evaluated whether limited-data machine learning could predict kapok fiber composition after aqueous chemical pretreatment using only three process descriptors. Kapok fibers were pretreated using dilute H2SO4, hydrothermal water, and NaOH across a shared window of residence time, temperature and pH-associated pretreatment environment. A data set of 33 pretreatment runs was constructed, with cellulose, hemicellulose and lignin contents used as composition targets after correction with solid yield. Single-output and wrapper-based multitarget regression models were screened and then evaluated using leave-one-out cross-validation to reduce dependence on a single train\u2013test split. The LOOCV results showed target-dependent predictability. In single-output modeling, cellulose was the most predictable component, with GBDT giving the strongest performance (R2 = 0.782; RMSE = 4.185 percentage points), whereas hemicellulose and lignin showed weaker prediction. In wrapper-based multitarget modeling, GBDT improved simultaneous prediction of cellulose, hemicellulose and lignin, giving R2 values of 0.909, 0.653, and 0.672, respectively. Feature-importance analysis indicated that temperature was the dominant descriptor for polysaccharide-related composition changes, whereas the pH-associated pretreatment environment contributed more strongly to lignin-related variation. These findings position limited-data machine learning as a useful exploratory tool for modeling pretreatment\u2013composition relationships in kapok fiber, while also highlighting the need for cautious validation when sample size is small.<\/jats:p>","DOI":"10.1021\/acsomega.6c04765","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T02:02:07Z","timestamp":1790215327000},"page":"59120-59130","source":"Crossref","is-referenced-by-count":0,"title":["Small Sample Machine Learning for Predicting Composition\nChanges in Chemically Pretreated Kapok Fiber"],"prefix":"10.1021","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6811-1936","authenticated-orcid":true,"given":"Yitong","family":"Niu","sequence":"first","affiliation":[{"name":"Universiti Sains Malaysia , , , ,","place":["Gelugor, Penang, Malaysia, 11800"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ireland","family":"LaBass","sequence":"additional","affiliation":[{"name":"Texas Woman\u2019s University , , , ,","place":["Denton, Texas, United States, 76204"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying Ying","family":"Tye","sequence":"additional","affiliation":[{"name":"Universiti Sains Malaysia , , , ,","place":["Gelugor, Penang, Malaysia, 11800"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tristan","family":"Smith","sequence":"additional","affiliation":[{"name":"Texas Woman\u2019s University , , , ,","place":["Denton, Texas, United States, 76204"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sicheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Texas Woman\u2019s University , , , ,","place":["Denton, Texas, United States, 76204"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8035-4690","authenticated-orcid":true,"given":"Yunxiang","family":"Li","sequence":"additional","affiliation":[{"name":"Texas Woman\u2019s University , , , ,","place":["Denton, Texas, United States, 76204"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4481-0446","authenticated-orcid":true,"given":"Ting","family":"Han","sequence":"additional","affiliation":[{"name":"Texas Woman\u2019s University , , , ,","place":["Denton, Texas, United States, 76204"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheu Peng","family":"Leh","sequence":"additional","affiliation":[{"name":"Universiti Sains Malaysia , , , ,","place":["Gelugor, Penang, Malaysia, 11800"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"issue":"10","key":"2026100610462620700_cit1","doi-asserted-by":"publisher","first-page":"3690","DOI":"10.1021\/acsfoodscitech.5c00557","article-title":"Advancements\nand the Prospects of Biodegradable Films Fabricated with Biological\nMacromolecules for Sustainable Food Packaging through Scientometric\nAnalysis: A Review","volume":"5","author":"S","year":"2025","journal-title":"ACS Food Sci. Technol."},{"issue":"43","key":"2026100610462620700_cit2","doi-asserted-by":"publisher","first-page":"59032","DOI":"10.1021\/acsami.5c16814","article-title":"Advances\nin Eco-Friendly Materials for Sustainable Packaging and Single-Use\nUtensils: A Decade of Innovation in Preparation, Characterization,\nand Applications","volume":"17","author":"Aguilar","year":"2025","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"2026100610462620700_cit3","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.biortech.2013.04.069","article-title":"Potential of Ceiba pentandra (L.) Gaertn. (Kapok) Fiber as a Resource\nfor Second Generation Bioethanol: Parametric Optimization and Comparative\nStudy of Various Pretreatments Prior Enzymatic Saccharification for\nSugar Production","volume":"140","author":"Tye","year":"2013","journal-title":"Bioresour. Technol."},{"key":"2026100610462620700_cit4","doi-asserted-by":"publisher","DOI":"10.1016\/j.carbpol.2019.115400","article-title":"The Influence of \u201cThermal\nDrying Pretreatment\u201d on Enzymatic Hydrolysis of Cellulose and\nXylan in Poplar Fibers with High Lignin Content","volume":"228","author":"Mo","year":"2020","journal-title":"Carbohydr. Polym."},{"issue":"4","key":"2026100610462620700_cit5","doi-asserted-by":"publisher","first-page":"393","DOI":"10.5658\/WOOD.2018.46.4.393","article-title":"Physical\nand Chemical Properties\nof Kapok (Ceiba Pentandra) and Balsa (Ochroma Pyramidale) Fibers","volume":"46","author":"Purnawati","year":"2018","journal-title":"J. Korean Wood Sci. Technol."},{"key":"2026100610462620700_cit6","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2025.136241","article-title":"Machine Learning-Driven\nOptimization\nof Biphasic Pretreatment Conditions for Enhanced Lignocellulosic Biomass\nFractionation","volume":"326","author":"Madadi","year":"2025","journal-title":"Energy"},{"key":"2026100610462620700_cit7","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1007\/s10570-025-06898-w","article-title":"Comparative Energy Assessment and\nNet Energy Return Analysis of Non-Wood Lignocellulosic Bioethanol\nSystems","volume":"33","author":"Niu","year":"2026","journal-title":"Cellulose"},{"key":"2026100610462620700_cit8","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.proenv.2014.03.041","article-title":"The Effect of Various\nPretreatment Methods on Oil Palm Empty Fruit Bunch (EFB) and Kenaf\nCore Fibers for Sugar Production","volume":"20","author":"Ying","year":"2014","journal-title":"Procedia Environ.\nSci."},{"issue":"1","key":"2026100610462620700_cit9","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-68879-6","article-title":"Comparison of Acid and Hydrothermal Pretreatments of\nDate Waste for Value Creation","volume":"14","author":"Oladzad","year":"2024","journal-title":"Sci. Rep."},{"issue":"1","key":"2026100610462620700_cit10","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1515\/gps-2021-0065","article-title":"Extraction and Modification\nof Hemicellulose from Lignocellulosic Biomass: A Review","volume":"10","author":"Lu","year":"2021","journal-title":"Green Process. Synth."},{"issue":"1","key":"2026100610462620700_cit11","doi-asserted-by":"publisher","DOI":"10.1186\/s13068-022-02130-0","article-title":"Effect of\nCombined Wet Alkaline Mechanical Pretreatment on Enzymatic Hydrolysis\nof Corn Stover and Its Mechanism","volume":"15","author":"Yang","year":"2022","journal-title":"Biotechnol.\nBiofuels Bioprod."},{"key":"2026100610462620700_cit12","doi-asserted-by":"publisher","DOI":"10.1016\/j.indcrop.2020.112145","article-title":"Effect of Sodium Hydroxide Pretreatment\non Physicochemical Changes and Enzymatic Hydrolysis of Herbaceous\nand Woody Lignocelluloses","volume":"145","author":"Wang","year":"2020","journal-title":"Ind. Crops Prod."},{"issue":"10","key":"2026100610462620700_cit13","doi-asserted-by":"publisher","first-page":"2314","DOI":"10.1021\/acssusresmgt.4c00348","article-title":"Agro-Waste from Gladiolus hybrida Plants: Effects of Alkaline Processing\non a New Natural Cellulosic Fiber Derived for Polymer Composites","volume":"1","author":"Shibly","year":"2024","journal-title":"ACS Sustainable Resour. Manage."},{"key":"2026100610462620700_cit14","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.jhazmat.2015.03.040","article-title":"Adsorption\nand Adhesiveness of Kapok Fiber to Different\nOils","volume":"296","author":"Dong","year":"2015","journal-title":"J. Hazard. Mater."},{"issue":"2","key":"2026100610462620700_cit15","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1039\/C7SE00481H","article-title":"Hollow\nCarbon Microtubes from Kapok Fiber: Structural Evolution and Energy\nStorage Performance","volume":"2","author":"Cao","year":"2018","journal-title":"Sustainable Energy Fuels"},{"issue":"4","key":"2026100610462620700_cit16","doi-asserted-by":"publisher","first-page":"1751","DOI":"10.1039\/D5SU00068H","article-title":"Comparative Analysis\nof Calotropis procera and Ceiba pentandra Fibre-Based Filters\nUsed to Separate Oil from Emulsified Effluent","volume":"3","author":"Singh","year":"2025","journal-title":"RSC Sustainability"},{"issue":"1","key":"2026100610462620700_cit17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.7324\/JABB.2023.110101","article-title":"Ceiba Pentandra\n(L.) Gaertn.: An Overview of Its Botany,\nUses, Reproductive Biology, Pharmacological Properties, and Industrial\nPotentials","volume":"11","author":"Chan","year":"2022","journal-title":"J. App Biol. Biotech"},{"key":"2026100610462620700_cit18","doi-asserted-by":"publisher","DOI":"10.1016\/j.biortech.2021.126033","article-title":"Hydrothermal Pretreatment of Lignocellulosic\nBiomass for Hemicellulose Recovery","volume":"342","author":"Scapini","year":"2021","journal-title":"Bioresour.\nTechnol."},{"key":"2026100610462620700_cit19","doi-asserted-by":"publisher","DOI":"10.1016\/j.fuel.2025.134682","article-title":"Machine Learning-Driven\nOptimization of Pretreatment and Enzymatic Hydrolysis of Sugarcane\nBagasse: Analytical Insights for Industrial Scale-Up","volume":"390","author":"Azad","year":"2025","journal-title":"Fuel"},{"key":"2026100610462620700_cit20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eja.2024.127297","article-title":"Improving Carbon Flux Estimation in Tea Plantation\nEcosystems: A Machine Learning Ensemble Approach","volume":"160","author":"Raza","year":"2024","journal-title":"Eur. J. Agron."},{"issue":"4","key":"2026100610462620700_cit21","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1007\/s10068-017-0119-x","article-title":"Prediction\nof Black Tea Fermentation Quality Indices Using NIRS and Nonlinear\nTools","volume":"26","author":"Dong","year":"2017","journal-title":"Food Sci. Biotechnol."},{"issue":"2","key":"2026100610462620700_cit22","doi-asserted-by":"publisher","DOI":"10.1111\/jfpe.12983","article-title":"Novel Prediction\nof Heavy Metal Residues\nin Fish Using a Low-Cost Optical Electronic Tongue System Based on\nColorimetric Sensors Array","volume":"42","author":"Han","year":"2019","journal-title":"J. Food Process\nEng."},{"key":"2026100610462620700_cit23","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/978-981-96-1578-0_15","article-title":"Kapok Pod Fibre as a Sustainable\nBiofuel Resource: Prediction of Cellulose Hydrolysis Using a Heuristic\nAlgorithm Optimized Random Forest","volume-title":"Proceedings\nof the 4th International Symposium in Environmental Science and Industrial\nEcology","author":"Niu","year":"2025"},{"key":"2026100610462620700_cit24","doi-asserted-by":"publisher","DOI":"10.1016\/j.biombioe.2025.107956","article-title":"Feasibility of Use\nLimited Data to Establish a Relationship between Chemical Composition\nand the Enzymatic Glucose Yield Using Machine Learning","volume":"200","author":"Niu","year":"2025","journal-title":"Biomass Bioenergy"},{"key":"2026100610462620700_cit25","doi-asserted-by":"publisher","DOI":"10.3389\/ffuel.2025.1722932","article-title":"Composition-Centered\nPrediction of Kenaf Core Saccharification for next-Generation Bioethanol\nvia Machine Learning","volume":"3","author":"Niu","year":"2025","journal-title":"Front. Fuels"},{"issue":"16","key":"2026100610462620700_cit26","doi-asserted-by":"publisher","first-page":"24461","DOI":"10.1021\/acsomega.6c00476","article-title":"Composition Retention\nMetrics Reveal Route-Specific Controls of Glucose Release from Pretreated\nKenaf Core","volume":"11","author":"Niu","year":"2026","journal-title":"ACS Omega"},{"key":"2026100610462620700_cit27","doi-asserted-by":"publisher","first-page":"1642","DOI":"10.1080\/17597269.2026.2633456","article-title":"Chemical Composition\nRetention Perspective: Research on Pretreatment Production of Lignocellulose\nBioethanol","volume":"17","author":"Niu","year":"2026","journal-title":"Biofuels"},{"key":"2026100610462620700_cit28","doi-asserted-by":"publisher","DOI":"10.1016\/j.esd.2025.101922","article-title":"Time-Resolved Co-Fermentation Dynamics: Optimizing\nFermentation Time for Energy-Efficient Lignocellulosic Ethanol Production\nfrom Oil Palm Empty Fruit Bunches","volume":"92","author":"Niu","year":"2026","journal-title":"Energy Sustainable\nDev."},{"key":"2026100610462620700_cit29","unstructured":"Tappi, T.\n          \n          Acid-Insoluble\nLignin in Wood and Pulp, TAPPI Test Methods; 2006."},{"key":"2026100610462620700_cit30","article-title":"Methods for\nthe Chemical Analysis of Biomass Process Streams","volume-title":"Handbook on Bioethanol","author":"Ehrman","year":"1996"},{"issue":"1","key":"2026100610462620700_cit31","doi-asserted-by":"publisher","DOI":"10.3390\/technologies11010024","article-title":"An Advanced\nDecision Tree-Based Deep Neural Network in Nonlinear Data Classification","volume":"11","author":"Arifuzzaman","year":"2023","journal-title":"Technologies"},{"issue":"5","key":"2026100610462620700_cit32","doi-asserted-by":"publisher","DOI":"10.3390\/e24050687","article-title":"Feature Importance in Gradient Boosting\nTrees with\nCross-Validation Feature Selection","volume":"24","author":"Adler","year":"2022","journal-title":"Entropy"},{"issue":"1","key":"2026100610462620700_cit33","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1080\/21642583.2014.956265","article-title":"Random Forests:\nFrom Early Developments\nto Recent Advancements","volume":"2","author":"Fawagreh","year":"2014","journal-title":"Syst. Sci. Control Eng."},{"key":"2026100610462620700_cit34","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/978-1-4302-5990-9_4","article-title":"Support Vector\nRegression","volume-title":"Efficient Learning Machines:\nTheories, Concepts, and Applications for Engineers and System Designers","author":"Awad","year":"2015"},{"key":"2026100610462620700_cit35","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/B978-0-12-815739-8.00007-9","article-title":"Support Vector\nRegression","volume-title":"Machine Learning","author":"Zhang","year":"2020"},{"issue":"3","key":"2026100610462620700_cit36","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/S0144-8617(02)00183-2","article-title":"Degradation of Cellulose\nunder Alkaline Conditions","volume":"51","author":"Knill","year":"2003","journal-title":"Carbohydr. Polym."},{"issue":"1","key":"2026100610462620700_cit37","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.15376\/biores.12.1.1263-1272","article-title":"Influencing Factors\nfor Alkaline Degradation of Cellulose","volume":"12","author":"Li","year":"2016","journal-title":"BioResources"},{"key":"2026100610462620700_cit38","doi-asserted-by":"publisher","DOI":"10.1016\/j.biortech.2021.125961","article-title":"Severity Factor Kinetic Model as a Strategic Parameter\nof Hydrothermal Processing (Steam Explosion and Liquid Hot Water)\nfor Biomass Fractionation under Biorefinery Concept","volume":"342","author":"Ruiz","year":"2021","journal-title":"Bioresour. Technol."},{"key":"2026100610462620700_cit39","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1016\/j.biortech.2017.09.155","article-title":"Hydrothermal\nExtraction of Hemicellulose: From Lab to Pilot Scale","volume":"247","author":"Gallina","year":"2018","journal-title":"Bioresour. Technol."},{"issue":"24","key":"2026100610462620700_cit40","doi-asserted-by":"publisher","DOI":"10.1002\/slct.202406050","article-title":"Hemicelluloses:\nA Review on Extraction and Modification for Various Applications","volume":"10","author":"Gautam","year":"2025","journal-title":"ChemistrySelect"},{"issue":"1","key":"2026100610462620700_cit41","doi-asserted-by":"publisher","DOI":"10.1186\/1754-6834-6-110","article-title":"The Fate of Lignin\nduring Hydrothermal\nPretreatment","volume":"6","author":"Trajano","year":"2013","journal-title":"Biotechnol. Biofuels"},{"issue":"32","key":"2026100610462620700_cit42","doi-asserted-by":"publisher","first-page":"9348","DOI":"10.1002\/slct.201801837","article-title":"Topochemical\nUnderstanding of Lignin Distribution During Hydrothermal Flowthrough\nPretreatment","volume":"3","author":"Jung","year":"2018","journal-title":"ChemistrySelect"},{"key":"2026100610462620700_cit43","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.biortech.2012.04.037","article-title":"Pseudo-Lignin Formation and Its Impact\non Enzymatic Hydrolysis","volume":"117","author":"Hu","year":"2012","journal-title":"Bioresour. Technol."},{"key":"2026100610462620700_cit44","doi-asserted-by":"publisher","DOI":"10.1016\/j.biortech.2021.126533","article-title":"Efficient\nRemoval of Surface-Deposited Pseudo-Lignin and Lignin Droplets by\nIsothermal Phase Separation during Hydrolysis","volume":"345","author":"Wu","year":"2022","journal-title":"Bioresour. Technol."},{"issue":"2","key":"2026100610462620700_cit45","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1002\/bit.25009","article-title":"Severity Factor\nCoefficients for Subcritical Liquid Hot Water Pretreatment of Hardwood\nChips","volume":"111","author":"Kim","year":"2014","journal-title":"Biotechnol. Bioeng."},{"key":"2026100610462620700_cit46","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.biortech.2018.12.073","article-title":"Effect of Severity\nFactor on the Hydrothermal Pretreatment\nof Sugarcane Straw","volume":"275","author":"Batista","year":"2019","journal-title":"Bioresour. Technol."},{"key":"2026100610462620700_cit47","doi-asserted-by":"publisher","first-page":"4246","DOI":"10.1016\/j.csbj.2025.09.027","article-title":"Predictive\nReducing Sugar Release from Lignocellulosic\nBiomass Using Sequential Acid Pretreatment and Enzymatic Hydrolysis\nby Harnessing a Machine Learning Approach","volume":"27","author":"Namboonlue","year":"2025","journal-title":"Comput.\nStruct. Biotechnol. J."},{"key":"2026100610462620700_cit48","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2020.104010","article-title":"Prediction\nof Nanofluids Viscosity Using Random Forest (RF) Approach","volume":"201","author":"Gholizadeh","year":"2020","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"2026100610462620700_cit49","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1016\/j.biortech.2015.06.011","article-title":"Mechanical Pretreatment\nImproving Hemicelluloses Removal from Cellulosic Fibers during Cold\nCaustic Extraction","volume":"192","author":"Li","year":"2015","journal-title":"Bioresour. Technol."},{"key":"2026100610462620700_cit50","doi-asserted-by":"publisher","DOI":"10.1016\/j.indcrop.2025.121644","article-title":"Machine Learning Models\nfor Predicting Enzymatic Hydrolysis Yields\nof Lignocellulosic Biomass after Various Pretreatments","volume":"235","author":"Xie","year":"2025","journal-title":"Ind. Crops Prod."},{"issue":"6","key":"2026100610462620700_cit51","doi-asserted-by":"publisher","first-page":"2541","DOI":"10.1021\/acs.biomac.3c00053","article-title":"Formation\nand Identification of Lignin\u2013Carbohydrate\nComplexes in Pre-Hydrolysis Liquors","volume":"24","author":"Feng","year":"2023","journal-title":"Biomacromolecules"},{"key":"2026100610462620700_cit52","doi-asserted-by":"publisher","DOI":"10.1016\/j.indcrop.2021.114108","article-title":"Pretreatment of Sugarcane Bagasse with Deep Eutectic Solvents Affect\nthe Structure and Morphology of Lignin","volume":"173","author":"Ji","year":"2021","journal-title":"Ind.\nCrop. Prod."},{"key":"2026100610462620700_cit53","doi-asserted-by":"publisher","DOI":"10.1016\/j.indcrop.2020.112357","article-title":"Efficient\nRemoval of Lignin from Vegetable Wastes by Ultrasonic and Microwave-Assisted\nTreatment with Ternary Deep Eutectic Solvent","volume":"149","author":"Ji","year":"2020","journal-title":"Ind. Crops Prod."},{"key":"2026100610462620700_cit54","doi-asserted-by":"publisher","DOI":"10.1016\/j.indcrop.2022.116034","article-title":"Utilization\nof Walnut Shell by Deep Eutectic Solvents: Enzymatic Digestion of\nCellulose and Preparation of Lignin Nanoparticles","volume":"192","author":"Li","year":"2023","journal-title":"Ind. Crops Prod."},{"key":"2026100610462620700_cit55","doi-asserted-by":"publisher","DOI":"10.3389\/fenrg.2018.00141","article-title":"Recent Trends in the\nPretreatment\nof Lignocellulosic Biomass for Value-Added Products","volume":"6","author":"Baruah","year":"2018","journal-title":"Front. Energy Res."},{"issue":"7","key":"2026100610462620700_cit56","doi-asserted-by":"publisher","first-page":"2058","DOI":"10.1080\/01969722.2025.2524076","article-title":"AD-VGF: An Improved Generative Adversarial\nNetwork for Credit Card\nRisk Identification and Management in Digital Economy","volume":"57","author":"Zhang","year":"2025","journal-title":"Cybern. Syst."},{"issue":"1","key":"2026100610462620700_cit57","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-31352-x","article-title":"Decoupled Temperature and Pressure Hydrothermal Synthesis of Carbon\nSub-Micron Spheres from Cellulose","volume":"13","author":"Yu","year":"2022","journal-title":"Nat. Commun."},{"issue":"6","key":"2026100610462620700_cit58","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1016\/j.biortech.2004.06.025","article-title":"Features of Promising Technologies\nfor Pretreatment of Lignocellulosic Biomass","volume":"96","author":"Mosier","year":"2005","journal-title":"Bioresour. Technol."},{"issue":"2","key":"2026100610462620700_cit59","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.15376\/biores.16.2.2524-2547","article-title":"An Integrated\nApproach for Hemicellulose Extraction from Forest Residue","volume":"16","author":"Ghosh","year":"2021","journal-title":"BioResources"},{"issue":"1","key":"2026100610462620700_cit60","doi-asserted-by":"publisher","DOI":"10.1186\/s13068-018-1262-1","article-title":"Lignin\u2013Carbohydrate\nComplexes:\nProperties, Applications, Analyses, and Methods of Extraction: A Review","volume":"11","author":"Tarasov","year":"2018","journal-title":"Biotechnol. Biofuels"},{"issue":"36","key":"2026100610462620700_cit61","doi-asserted-by":"publisher","first-page":"10116","DOI":"10.1021\/acs.jafc.9b02851","article-title":"How Pseudo-Lignin\nIs Generated during\nDilute Sulfuric Acid Pretreatment","volume":"67","author":"Wan","year":"2019","journal-title":"J. Agric.\nFood Chem."},{"issue":"10","key":"2026100610462620700_cit62","doi-asserted-by":"publisher","first-page":"2192","DOI":"10.1039\/C8GC00353J","article-title":"Recent\nAdvances in Understanding the Pseudo-Lignin Formation in a Lignocellulosic\nBiorefinery","volume":"20","author":"Shinde","year":"2018","journal-title":"Green Chem."},{"key":"2026100610462620700_cit63","doi-asserted-by":"crossref","unstructured":"Wang, J.; Deng, J.; Rashwan, T. L.; Qu, G.; Ren, S.; Liu, D.\n          Oxygen-Lean Thermokinetics and Early-Warning of Laojunmiao\nNon-Caking Coal in Xinjiang Coalfield\n          Int.\nJ. Min. Sci. Technol.\n          2026\n          10.1016\/j.ijmst.2026.07.005.","DOI":"10.1016\/j.ijmst.2026.07.005"},{"key":"2026100610462620700_cit64","doi-asserted-by":"publisher","DOI":"10.1002\/adma.74394","article-title":"Atomic Editing of the\nFirst-Shell to Modulate the d-Electron State of Transition Metal Sites\nfor Accelerated Redox Kinetics in Lithium\u2013Sulfur Batteries","volume":"38","author":"Zhao","year":"2026","journal-title":"Adv. Mater."},{"key":"2026100610462620700_cit65","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2026.113122","article-title":"Reliability\nof Wireless Sensor Networks\nwith Probabilistic Functional Dependence: A Lifetime-Based Approach","volume":"277","author":"Da","year":"2027","journal-title":"Reliab. Eng. Syst. Safety"}],"container-title":["ACS Omega"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acsomega.6c04765","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/acsodf\/article-pdf\/11\/39\/59120\/68630220\/acsomega.6c04765.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/acsodf\/article-pdf\/11\/39\/59120\/68630220\/acsomega.6c04765.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T15:22:33Z","timestamp":1791300153000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/acsodf\/article\/11\/39\/59120\/5437164\/Small-Sample-Machine-Learning-for-Predicting"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,23]]},"references-count":65,"journal-issue":{"issue":"39","published-online":{"date-parts":[[2026,9,23]]},"published-print":{"date-parts":[[2026,10,6]]}},"URL":"https:\/\/doi.org\/10.1021\/acsomega.6c04765","relation":{},"ISSN":["2470-1343"],"issn-type":[{"value":"2470-1343","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,23]]}}}