{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T12:49:55Z","timestamp":1792500595824,"version":"4.5.0"},"reference-count":49,"publisher":"American Chemical Society (ACS)","issue":"41","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001700","name":"Ministry of Education, Culture, Sports, Science and Technology","doi-asserted-by":"publisher","award":["JPJ011438"],"award-info":[{"award-number":["JPJ011438"]}],"id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006151","name":"Basic Energy Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006151","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","award":["JPMJAP2312"],"award-info":[{"award-number":["JPMJAP2312"]}],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["863691"],"award-info":[{"award-number":["863691"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000078","name":"Division of Materials Research","doi-asserted-by":"publisher","award":["DMR-2309083"],"award-info":[{"award-number":["DMR-2309083"]}],"id":[{"id":"10.13039\/100000078","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Self-driving laboratories or autonomous experimentation are emerging as transformative platforms for accelerating scientific discovery. Bayesian optimization (BO) is among the most widely used machine learning frameworks in self-driving laboratories, but these BO-based frameworks rely on predefined scalar descriptors to guide experimentation. This limits their ability to handle complex phenomena that are difficult to formalize as scalar descriptors. In many situations, the determination of an appropriate scalar descriptor can be challenging, and may fail to capture subtle yet scientifically important phenomena apparent to experts with interdisciplinary insight. This problem is particularly pronounced in nanoscale imaging experiments, where strong local heterogeneity, multidimensional observations, and emergent physical behaviors often cannot be effectively represented using scalar metrics. To overcome this limitation, here we develop deep-kernel pairwise learning (DKPL), an approach for autonomous microscopy experiments which incorporates human expertise and interdisciplinary scientific knowledge into an active learning loop. Instead of relying on explicit scalar objectives, DKPL enables experts to directly evaluate which experimental output is more promising using interdisciplinary knowledge. DKPL then learns a latent utility function from these expert judgments to guide subsequent autonomous microscopy experiments. We demonstrate DKPL\u2019s performance in learning physically meaningful nanoscale structures in lead titanate thin films while effectively prioritizing high-information measurement regions using an experimental model dataset with known ground truth. We further apply DKPL to analyze the character of ferroelectric domain walls, where we find DKPL capable of distinguishing between high and low characteristic domain-wall angles in lead zirconium titanate, and able to discover both head-to-head and tail-to-tail domain-wall character in erbium manganite. Autonomous experiments in mixed-phase bismuth ferrite also demonstrate DKPL\u2019s ability to link material structure with their properties in on-the-fly measurements. This development demonstrates an approach to integrate expert knowledge into autonomous microscopy experiments across a variety of material systems, and demonstrates a pathway toward expert-guided self-driving laboratories capable of addressing scientific problems beyond the limits of scalar-metrics-driven learning.<\/jats:p>","DOI":"10.1021\/acsnano.6c09449","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T15:01:48Z","timestamp":1791385308000},"page":"27774-27783","source":"Crossref","is-referenced-by-count":0,"title":["Beyond Scalar Objectives:\nExpert-Feedback-Driven Autonomous\nExperimentation for Scientific Discovery at the Nanoscale"],"prefix":"10.1021","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6779-4031","authenticated-orcid":true,"given":"Ralph","family":"Bulanadi","sequence":"first","affiliation":[{"name":"Oak Ridge National Laboratory , , , ,","place":["Oak Ridge, Tennessee, United States, 37831"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jefferey","family":"Baxter","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory , , , ,","place":["Oak Ridge, Tennessee, United States, 37831"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arpan","family":"Biswas","sequence":"additional","affiliation":[{"name":"University of Tennessee-Oak Ridge Innovation Institute, University of Tennessee , , ,","place":["Knoxville, Tennessee, United States, 37996"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Funakubo","sequence":"additional","affiliation":[{"name":"Institute of Science Tokyo , , ,","place":["Yokohama, Japan, 226-8502"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2534-5868","authenticated-orcid":true,"given":"Valanoor","family":"Nagarajan","sequence":"additional","affiliation":[{"name":"UNSW Sydney , , , ,","place":["Kensington, New South Wales, Australia, 2052"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8626-6912","authenticated-orcid":true,"given":"Daniel","family":"Sando","sequence":"additional","affiliation":[{"name":"UNSW Sydney , , , ,","place":["Kensington, New South Wales, Australia, 2052"]},{"name":"University of Canterbury , , ,","place":["Christchurch, New Zealand, 8410"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dennis","family":"Meier","sequence":"additional","affiliation":[{"name":"Norwegian University of Science and Technology (NTNU) , , ,","place":["Trondheim, Norway, N-7491"]},{"name":"University of Duisburg-Essen , , ,","place":["Duisburg, Germany, 47057"]},{"name":"Research Alliance Ruhr , , ,","place":["Bochum, Germany, 44780"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7389-1295","authenticated-orcid":true,"given":"Jan","family":"Schulthei\u00df","sequence":"additional","affiliation":[{"name":"Norwegian University of Science and Technology (NTNU) , , ,","place":["Trondheim, Norway, N-7491"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4692-8579","authenticated-orcid":true,"given":"Rama","family":"Vasudevan","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory , , , ,","place":["Oak Ridge, Tennessee, United States, 37831"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0152-1783","authenticated-orcid":true,"given":"Yongtao","family":"Liu","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory , , , ,","place":["Oak Ridge, Tennessee, United States, 37831"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"key":"2026102008164048200_cit1","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1038\/s44160-022-00231-0","article-title":"The rise of self-driving labs in chemical and materials\nsciences","volume":"2","author":"Abolhasani","year":"2023","journal-title":"Nat. Synth."},{"key":"2026102008164048200_cit2","doi-asserted-by":"publisher","first-page":"2301740","DOI":"10.1002\/smtd.202301740","article-title":"AEcroscopy: a software\u2013hardware\nframework empowering microscopy toward automated and autonomous experimentation","volume":"8","author":"Liu","year":"2024","journal-title":"Small Methods"},{"key":"2026102008164048200_cit3","doi-asserted-by":"publisher","first-page":"2454","DOI":"10.1021\/acs.accounts.2c00220","article-title":"Autonomous chemical experiments:\nChallenges and perspectives\non establishing a self-driving lab","volume":"55","author":"Seifrid","year":"2022","journal-title":"Acc. Chem.\nRes."},{"key":"2026102008164048200_cit4","doi-asserted-by":"publisher","first-page":"9633","DOI":"10.1021\/acs.chemrev.4c00055","article-title":"Self-driving laboratories for chemistry\nand materials science","volume":"124","author":"Tom","year":"2024","journal-title":"Chem. Rev."},{"key":"2026102008164048200_cit5","doi-asserted-by":"publisher","first-page":"13492","DOI":"10.1021\/acsnano.2c05303","article-title":"Bayesian active\nlearning for scanning probe microscopy: From Gaussian processes to\nhypothesis learning","volume":"16","author":"Ziatdinov","year":"2022","journal-title":"ACS Nano"},{"key":"2026102008164048200_cit6","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2604.04194","article-title":"PATHFINDER:\nMulti-objective discovery in structural and spectral spaces","author":"Barakati","year":"2026","journal-title":"arXiv"},{"key":"2026102008164048200_cit7","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2603.16715","article-title":"Novelty-Driven\nTarget-Space Discovery in Automated Electron and Scanning Probe Microscopy","author":"Pratiush","year":"2026","journal-title":"arXiv"},{"key":"2026102008164048200_cit8","doi-asserted-by":"publisher","first-page":"2403263","DOI":"10.1002\/aenm.202403263","article-title":"Autonomous\nbattery optimization by\ndeploying distributed experiments and simulations","volume":"14","author":"Vogler","year":"2024","journal-title":"Adv. Energy Mater."},{"key":"2026102008164048200_cit9","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1038\/s44286-024-00033-5","article-title":"Autonomous\nreaction Pareto-front mapping with a self-driving\ncatalysis laboratory","volume":"1","author":"Bennett","year":"2024","journal-title":"Nat. Chem. Eng."},{"key":"2026102008164048200_cit10","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adj0461","article-title":"AI-driven\nrobotic chemist for autonomous synthesis\nof organic molecules","volume":"9","author":"Ha","year":"2023","journal-title":"Sci. Adv."},{"key":"2026102008164048200_cit11","doi-asserted-by":"publisher","first-page":"116780","DOI":"10.1016\/j.jeurceramsoc.2024.116780","article-title":"Review of the opportunities\nand limitations for powder-based high-throughput solid-state processing\nof advanced functional ceramics","volume":"44","author":"Webber","year":"2024","journal-title":"J. Eur. Ceram.\nSoc."},{"key":"2026102008164048200_cit12","doi-asserted-by":"publisher","first-page":"2301763","DOI":"10.1002\/smtd.202301763","article-title":"Autonomous synthesis of thin film\nmaterials with pulsed laser deposition enabled by in situ spectroscopy\nand automation","volume":"8","author":"Harris","year":"2024","journal-title":"Small Methods"},{"key":"2026102008164048200_cit13","doi-asserted-by":"publisher","first-page":"35493","DOI":"10.1021\/acsnano.5c09057","article-title":"Zero-Shot Autonomous Microscopy for Scalable and Intelligent Characterization\nof 2D Materials","volume":"19","author":"Yang","year":"2025","journal-title":"ACS Nano"},{"key":"2026102008164048200_cit14","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.1039\/D4DD00080C","article-title":"Physics-driven discovery and bandgap engineering of\nhybrid perovskites","volume":"3","author":"Sanchez","year":"2024","journal-title":"Digital Discovery"},{"key":"2026102008164048200_cit15","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2511.11558","article-title":"Human-AI collaborative autonomous synthesis with\npulsed laser deposition for remote epitaxy","author":"Haque","year":"2025","journal-title":"arXiv"},{"key":"2026102008164048200_cit16","doi-asserted-by":"publisher","first-page":"2404655","DOI":"10.1002\/aenm.202404655","article-title":"Tailoring Molecular\nSpace to Navigate Phase Complexity\nin Cs-Based Quasi-2D Perovskites via Gated-Gaussian-Driven High-Throughput\nDiscovery","volume":"15","author":"Um","year":"2025","journal-title":"Adv. Energy Mater."},{"key":"2026102008164048200_cit17","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1038\/s41524-024-01485-2","article-title":"Active oversight and quality control\nin standard Bayesian optimization for autonomous experiments","volume":"11","author":"Harris","year":"2025","journal-title":"Npj Comput. Mater."},{"key":"2026102008164048200_cit18","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1039\/D4DD00277F","article-title":"Scientific\nexploration with expert knowledge (SEEK) in autonomous scanning probe\nmicroscopy with active learning","volume":"4","author":"Pratiush","year":"2025","journal-title":"Digital Discovery"},{"key":"2026102008164048200_cit19","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1039\/D4DD00299G","article-title":"SANE: strategic\nautonomous non-smooth exploration for multiple optima\ndiscovery in multi-modal and non-differentiable black-box functions","volume":"4","author":"Biswas","year":"2025","journal-title":"Digital Discovery"},{"key":"2026102008164048200_cit20","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1038\/s41524-023-01191-5","article-title":"A dynamic\nBayesian optimized active recommender system for curiosity-driven\npartially Human-in-the-loop automated experiments","volume":"10","author":"Biswas","year":"2024","journal-title":"Npj Comput. Mater."},{"key":"2026102008164048200_cit21","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1910.06403","article-title":"BoTorch:\nA framework for efficient\nMonte-Carlo Bayesian optimization","author":"Balandat","year":"2020","journal-title":"arXiv"},{"key":"2026102008164048200_cit22","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/978-3-642-14125-6_4","article-title":"Preference\nlearning and ranking by pairwise comparison","volume-title":"Preference learning","author":"Fu\u0308rnkranz","year":"2010"},{"key":"2026102008164048200_cit23","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2603.12618","article-title":"Human-AI Collaborative Autonomous\nExperimentation With Proxy Modeling for Comparative Observation","author":"Biswas","year":"2026","journal-title":"arXiv"},{"key":"2026102008164048200_cit24","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2508.07126","article-title":"Pref-GUIDE: Continual\nPolicy\nLearning from Real-Time Human Feedback via Preference-Based Learning","author":"Ji","year":"2025","journal-title":"arXiv"},{"key":"2026102008164048200_cit25","doi-asserted-by":"publisher","first-page":"435503","DOI":"10.1088\/0957-4484\/18\/43\/435503","article-title":"The band excitation\nmethod in scanning probe microscopy for rapid mapping of energy dissipation\non the nanoscale","volume":"18","author":"Jesse","year":"2007","journal-title":"Nanotechnology"},{"key":"2026102008164048200_cit26","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1146\/annurev-physchem-040513-103609","article-title":"Band excitation\nin scanning probe microscopy: recognition and functional imaging","volume":"65","author":"Jesse","year":"2014","journal-title":"Annu. Rev. Phys. Chem."},{"key":"2026102008164048200_cit27","doi-asserted-by":"publisher","first-page":"062908","DOI":"10.1063\/1.2172216","article-title":"Switching\nspectroscopy piezoresponse\nforce microscopy of ferroelectric materials","volume":"88","author":"Jesse","year":"2006","journal-title":"Appl. Phys. Lett."},{"key":"2026102008164048200_cit28","doi-asserted-by":"publisher","first-page":"101101","DOI":"10.1016\/j.pmatsci.2023.101101","article-title":"Ferroelectric\npolycrystals: Structural\nand microstructural levers for property-engineering via domain-wall\ndynamics","volume":"136","author":"Schulthei\u00df","year":"2023","journal-title":"Prog. Mater. Sci."},{"key":"2026102008164048200_cit29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/jace.12773","article-title":"Decoding the fingerprint of ferroelectric\nloops: comprehension\nof the material properties and structures","volume":"97","author":"Jin","year":"2014","journal-title":"J.\nAm. Ceram. Soc."},{"key":"2026102008164048200_cit30","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1038\/s42256-022-00460-0","article-title":"Experimental discovery of structure\u2013property\nrelationships in ferroelectric materials via active learning","volume":"4","author":"Liu","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"2026102008164048200_cit31","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1038\/s41524-023-00985-x","article-title":"Learning the right channel in multimodal imaging: automated\nexperiment in piezoresponse force microscopy","volume":"9","author":"Liu","year":"2023","journal-title":"Npj Comput. Mater."},{"key":"2026102008164048200_cit32","doi-asserted-by":"publisher","first-page":"3352","DOI":"10.1021\/acs.jpclett.3c00223","article-title":"Exploring\nthe relationship of microstructure\nand conductivity in metal halide perovskites via active learning-driven\nautomated scanning probe microscopy","volume":"14","author":"Liu","year":"2023","journal-title":"J. Phys.\nChem. Lett."},{"key":"2026102008164048200_cit33","doi-asserted-by":"publisher","first-page":"2204130","DOI":"10.1002\/smll.202204130","article-title":"Automated experiments of local non-linear\nbehavior in ferroelectric materials","volume":"18","author":"Liu","year":"2022","journal-title":"Small"},{"key":"2026102008164048200_cit34","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1021\/acsnanoscienceau.5c00106","article-title":"Beyond optimization:\nExploring novelty discovery in autonomous experiments","volume":"6","author":"Bulanadi","year":"2025","journal-title":"ACS Nanosci. Au"},{"issue":"9","key":"2026102008164048200_cit35","doi-asserted-by":"publisher","first-page":"2672","DOI":"10.1021\/acsmaterialslett.6c00558","article-title":"Automated\nThree-Dimensional Piezoresponse Force Microscopy for Polarization-Vector\nMapping at the Nanoscale","volume":"8","author":"Bulanadi","year":"2026","journal-title":"ACS Mater. Lett."},{"key":"2026102008164048200_cit36","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1038\/s41578-021-00375-z","article-title":"Ferroelectric domain\nwalls for nanotechnology","volume":"7","author":"Meier","year":"2022","journal-title":"Nat. Rev. Mater."},{"key":"2026102008164048200_cit37","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1103\/RevModPhys.84.119","article-title":"Domain wall nanoelectronics","volume":"84","author":"Catalan","year":"2012","journal-title":"Rev. Modern Phys."},{"issue":"3","key":"2026102008164048200_cit38","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1038\/nmat2373","article-title":"Conduction\nat domain walls in oxide multiferroics","volume":"8","author":"Seidel","year":"2009","journal-title":"Nat.\nMater."},{"key":"2026102008164048200_cit39","doi-asserted-by":"publisher","first-page":"2110263","DOI":"10.1002\/adfm.202110263","article-title":"Roadmap for ferroelectric\ndomain wall nanoelectronics","volume":"32","author":"Sharma","year":"2022","journal-title":"Adv. Funct. Mater."},{"key":"2026102008164048200_cit40","doi-asserted-by":"publisher","first-page":"2203449","DOI":"10.1002\/adma.202203449","article-title":"Confinement-driven inverse domain\nscaling in polycrystalline ErMnO3","volume":"34","author":"Schulthei\u00df","year":"2022","journal-title":"Adv. Mater."},{"key":"2026102008164048200_cit41","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1038\/nmat3249","article-title":"Anisotropic\nconductance at improper ferroelectric domain walls","volume":"11","author":"Meier","year":"2012","journal-title":"Nat. Mater."},{"key":"2026102008164048200_cit42","doi-asserted-by":"publisher","first-page":"6381","DOI":"10.1021\/acs.nanolett.8b02742","article-title":"Large carrier mobilities\nin ErMnO3 conducting domain walls revealed by quantitative\nHall-effect measurements","volume":"18","author":"Turner","year":"2018","journal-title":"Nano Lett."},{"key":"2026102008164048200_cit43","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2607.22975","article-title":"AEcroscopyWave:\nTowards Self-Driving\nCharacterization Platforms for Agentic AI","author":"Liu","year":"2026","journal-title":"arXiv"},{"key":"2026102008164048200_cit44","doi-asserted-by":"publisher","first-page":"011106","DOI":"10.1063\/1.4944558","article-title":"A multiferroic on the brink: Uncovering\nthe nuances of strain-induced transitions in BiFeO3","volume":"3","author":"Sando","year":"2016","journal-title":"Appl. Phys. Rev."},{"key":"2026102008164048200_cit45","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1126\/science.1177046","article-title":"A strain-driven\nmorphotropic phase\nboundary in BiFeO3","volume":"326","author":"Zeches","year":"2009","journal-title":"Science"},{"key":"2026102008164048200_cit46","doi-asserted-by":"publisher","DOI":"10.64898\/2026.06.06.730554","article-title":"APOSM:\nPairwise\npreference learning improves generative small-molecule design","author":"Dreisler","year":"2026","journal-title":"bioRxiv"},{"key":"2026102008164048200_cit47","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1039\/D6DD00009F","article-title":"Jeweler-in-the-loop: personalized\nalloy color optimization\nvia preference-based BO","volume":"5","author":"Katz","year":"2026","journal-title":"Digital Discovery"},{"key":"2026102008164048200_cit48","doi-asserted-by":"publisher","first-page":"2617","DOI":"10.1021\/acselectrochem.5c00377","article-title":"Opportunities\nof large language models\nin the development of organic electrosynthesis","volume":"1","author":"Yu","year":"2025","journal-title":"ACS Electrochem."},{"key":"2026102008164048200_cit49","unstructured":"Bulanadi, R.; Liu, Y.\n          Deep Kernel Pairwise Learning; 2026. https:\/\/github.com\/rbulanadi\/DeepKernelPairwiseLearning (accessed September 16, 2026)."}],"container-title":["ACS Nano"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acsnano.6c09449","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/ancac3\/article-pdf\/20\/41\/27774\/68772156\/acsnano.6c09449.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/ancac3\/article-pdf\/20\/41\/27774\/68772156\/acsnano.6c09449.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T12:29:48Z","timestamp":1792499388000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/ancac3\/article\/20\/41\/27774\/5446141\/Beyond-Scalar-Objectives-Expert-Feedback-Driven"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,7]]},"references-count":49,"journal-issue":{"issue":"41","published-online":{"date-parts":[[2026,10,7]]},"published-print":{"date-parts":[[2026,10,20]]}},"URL":"https:\/\/doi.org\/10.1021\/acsnano.6c09449","relation":{},"ISSN":["1936-0851","1936-086X"],"issn-type":[{"value":"1936-0851","type":"print"},{"value":"1936-086X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,7]]}}}