{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:32:41Z","timestamp":1774679561571,"version":"3.50.1"},"reference-count":39,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001871","name":"FCT","doi-asserted-by":"publisher","award":["UID\/50011\/2025 (DOI 10.54499\/UID\/50011\/2025)"],"award-info":[{"award-number":["UID\/50011\/2025 (DOI 10.54499\/UID\/50011\/2025)"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT","doi-asserted-by":"publisher","award":["LA\/P\/0006\/2020 (DOI 10.54499\/LA\/P\/0006\/2020)"],"award-info":[{"award-number":["LA\/P\/0006\/2020 (DOI 10.54499\/LA\/P\/0006\/2020)"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT","doi-asserted-by":"publisher","award":["2023.16112.ICDT"],"award-info":[{"award-number":["2023.16112.ICDT"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["adv. intell. discov."],"abstract":"<jats:p>\n                    Transient heating curves obtained from luminescence thermometry encode valuable information about nanoscale heat transport, yet extracting the onset time reliably remains challenging due to experimental noise and the need for manual signal\u2010processing pipelines. Here, we introduce a machine\u2010learning\u2010assisted framework for automated analysis of transient heating curves measured using Ln\n                    <jats:sup>3+<\/jats:sup>\n                    \u2010doped upconverting nanoparticles. Neural\u2010network models spanning several architectural families are trained using a balanced dataset combining 822 experimental transients with 822 physically motivated synthetic curves designed to extend the diversity of heating dynamics beyond experimentally accessible variability. We show that predictive robustness is governed primarily by the diversity and physical plausibility of the training data rather than by architectural complexity alone. Relative to a conventional discrete wavelet transform reference procedure, the best\u2010performing models achieve median onset\u2010time errors on the order of 2\u2009s, and once trained, determine the onset time with sub\u2010second latency per transient curve. The resulting framework enables fully automated, operator\u2010independent analysis of transient luminescence signals and provides a scalable strategy for extracting thermal metrics from time\u2010resolved spectroscopic data. More broadly, this approach establishes a pathway toward data\u2010driven analysis of dynamic thermal processes in nanoscale systems and intelligent materials with integrated sensing capabilities.\n                  <\/jats:p>","DOI":"10.1002\/aidi.202600003","type":"journal-article","created":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T04:50:31Z","timestamp":1774673431000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine\u2010Learning\u2010Assisted Onset\u2010Time Determination in Transient Luminescence Thermometry"],"prefix":"10.1002","author":[{"given":"David J.","family":"Sousa","sequence":"first","affiliation":[{"name":"Phantom\u2010g CICECO\u2013Aveiro Institute of Materials Physics Department University of Aveiro  Aveiro Portugal"}]},{"given":"Erving","family":"Ximendes","sequence":"additional","affiliation":[{"name":"Nanomaterials for Bioimaging Group (nanoBIG) Department of Materials Physics Faculty of Sciences Universidad Aut\u00f3noma de Madrid, and Instituto Ram\u00f3n y Cajal de Investigaci\u00f3n Sanitaria (IRYCIS) Hospital Ram\u00f3n y Cajal  Madrid Spain"}]},{"given":"Lu\u00eds D.","family":"Carlos","sequence":"additional","affiliation":[{"name":"Phantom\u2010g CICECO\u2013Aveiro Institute of Materials Physics Department University of Aveiro  Aveiro Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9636-2628","authenticated-orcid":false,"given":"Carlos D. S.","family":"Brites","sequence":"additional","affiliation":[{"name":"Phantom\u2010g CICECO\u2013Aveiro Institute of Materials Physics Department University of Aveiro  Aveiro Portugal"}]}],"member":"311","published-online":{"date-parts":[[2026,3,27]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1038\/nature14541","article-title":"Probabilistic Machine Learning and Artificial Intelligence","volume":"521","author":"Ghahramani Z.","year":"2015","journal-title":"Nature"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","first-page":"20190054","DOI":"10.1098\/rsta.2019.0054","article-title":"Machine Learning and Big Scientific Data","volume":"378","author":"Hey T.","year":"2020","journal-title":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences"},{"key":"e_1_2_10_4_1","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real\u2010World Applications and Research Directions","volume":"2","author":"Sarker I. H.","year":"2021","journal-title":"SN Computer Science"},{"key":"e_1_2_10_5_1","doi-asserted-by":"crossref","first-page":"35365","DOI":"10.1109\/ACCESS.2018.2836950","article-title":"Machine Learning and Deep Learning Methods for Cybersecurity","volume":"6","author":"Xin Y.","year":"2018","journal-title":"IEEE Access"},{"key":"e_1_2_10_6_1","doi-asserted-by":"crossref","first-page":"120802","DOI":"10.1016\/j.ins.2024.120802","article-title":"Intelligent Medical Diagnosis and Treatment for Diabetes with Deep Convolutional Fuzzy Neural Networks","volume":"677","author":"Zhou W. H.","year":"2024","journal-title":"Information Sciences"},{"key":"e_1_2_10_7_1","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s10479-019-03144-y","article-title":"Neural Networks in Financial Trading","volume":"297","author":"Sermpinis G.","year":"2021","journal-title":"Annals of Operations Research"},{"key":"e_1_2_10_8_1","doi-asserted-by":"crossref","first-page":"3469","DOI":"10.1007\/s00521-017-3285-0","article-title":"Improving Optimization of Convolutional Neural Networks through Parameter Fine\u2010Tuning","volume":"31","author":"Becherer N.","year":"2019","journal-title":"Neural Computing and Applications"},{"key":"e_1_2_10_9_1","doi-asserted-by":"crossref","first-page":"9945","DOI":"10.3390\/s22249945","article-title":"Domain Adaptation with Augmented Data by Deep Neural Network Based Method Using Re\u2010Recorded Speech for Automatic Speech Recognition in Real Environment","volume":"22","author":"Nahar R.","year":"2022","journal-title":"Sensors"},{"key":"e_1_2_10_10_1","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1613\/jair.4992","article-title":"A Primer on Neural Network Models for Natural Language Processing","volume":"57","author":"Goldberg Y.","year":"2016","journal-title":"Journal of Artificial Intelligence Research"},{"key":"e_1_2_10_11_1","doi-asserted-by":"crossref","first-page":"119637","DOI":"10.1016\/j.jlumin.2022.119637","article-title":"Convolutional Neural Networks Open up Horizons for Luminescence Thermometry","volume":"256","author":"Cui J. Q.","year":"2023","journal-title":"Journal of Luminescence"},{"key":"e_1_2_10_12_1","doi-asserted-by":"crossref","first-page":"114666","DOI":"10.1016\/j.sna.2023.114666","article-title":"Towards Accurate Real\u2010Time Luminescence Thermometry: An Automated Machine Learning Approach","volume":"362","author":"Santos E. P.","year":"2023","journal-title":"Sensors and Actuators A: Physical"},{"key":"e_1_2_10_13_1","doi-asserted-by":"crossref","first-page":"e2306606","DOI":"10.1002\/adma.202306606","article-title":"Neural Networks Push the Limits of Luminescence Lifetime Nanosensing","volume":"35","author":"Ming L. Y.","year":"2023","journal-title":"Advanced Materials"},{"key":"e_1_2_10_14_1","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/978-3-031-94748-3_20","article-title":"From Birth to Loss of Representations in Artificial Neural Networks","volume":"15551","author":"Stecher P.","year":"2026","journal-title":"Lecture Notes in Computer Science"},{"key":"e_1_2_10_15_1","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1021\/acs.jpclett.0c03203","article-title":"Opportunities for Next\u2010Generation Luminescent Materials through Artificial Intelligence","volume":"12","author":"Zhuo Y.","year":"2021","journal-title":"The Journal of Physical Chemistry Letters"},{"key":"e_1_2_10_16_1","doi-asserted-by":"crossref","first-page":"5401","DOI":"10.1021\/acs.chemmater.3c00731","article-title":"Machine Learning\u2010Directed Predictive Models: Deciphering Complex Energy Transfer in Mn\u2010Doped CsPb(Cl1\u2010yBry)3 Perovskite Nanocrystals","volume":"35","author":"Choe H.","year":"2023","journal-title":"Chemistry of Materials"},{"key":"e_1_2_10_17_1","doi-asserted-by":"crossref","first-page":"2582","DOI":"10.3390\/diagnostics13152582","article-title":"What Is Machine Learning, Artificial Neural Networks and Deep Learning?\u2010Examples of Practical Applications in Medicine","volume":"13","author":"Kufel J.","year":"2023","journal-title":"Diagnostics"},{"key":"e_1_2_10_18_1","doi-asserted-by":"crossref","first-page":"27116","DOI":"10.1021\/acsnano.5c04200","article-title":"Artificial Intelligence for Materials Discovery, Development, and Optimization","volume":"19","author":"Madika B.","year":"2025","journal-title":"ACS Nano"},{"key":"e_1_2_10_19_1","doi-asserted-by":"crossref","first-page":"105037","DOI":"10.1016\/j.imavis.2024.105037","article-title":"Image Recognition Based on Lightweight Convolutional Neural Network: Recent Advances","volume":"146","author":"Liu Y.","year":"2024","journal-title":"Image and Vision Computing"},{"key":"e_1_2_10_20_1","doi-asserted-by":"crossref","first-page":"2302749","DOI":"10.1002\/adma.202302749","article-title":"Spotlight on Luminescence Thermometry: Basics, Challenges, and Cutting\u2010Edge Applications","volume":"35","author":"Brites C. D. S.","year":"2023","journal-title":"Advanced Materials"},{"key":"e_1_2_10_21_1","doi-asserted-by":"crossref","first-page":"1801239","DOI":"10.1002\/adom.201801239","article-title":"Lanthanide\u2010Based Thermometers: At the Cutting\u2010Edge of Luminescence Thermometry","volume":"7","author":"Brites C. D. S.","year":"2019","journal-title":"Advanced Optical Materials"},{"key":"e_1_2_10_22_1","doi-asserted-by":"crossref","first-page":"100797","DOI":"10.1016\/j.nantod.2019.100797","article-title":"Recent Advances in Upconversion Nanocrystals: Expanding the Kaleidoscopic Toolbox for Emerging Applications","volume":"29","author":"Zheng K. Z.","year":"2019","journal-title":"Nano Today"},{"key":"e_1_2_10_23_1","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1038\/s41592-020-0957-y","article-title":"Advances and Challenges for Fluorescence Nanothermometry","volume":"17","author":"Zhou J. J.","year":"2020","journal-title":"Nature Methods"},{"key":"e_1_2_10_24_1","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1021\/acsnanoscienceau.3c00051","article-title":"Luminescence Thermometry Beyond the Biological Realm","volume":"4","author":"Harrington B.","year":"2024","journal-title":"ACS Nanoscience Au"},{"key":"e_1_2_10_25_1","doi-asserted-by":"crossref","first-page":"4122","DOI":"10.1021\/acsnano.9b08824","article-title":"In Vivo Spectral Distortions of Infrared Luminescent Nanothermometers Compromise Their Reliability","volume":"14","author":"Shen Y. L.","year":"2020","journal-title":"ACS Nano"},{"key":"e_1_2_10_26_1","doi-asserted-by":"crossref","first-page":"3443","DOI":"10.1039\/D5QM00598A","article-title":"Combining Materials Design and Deep Learning: AI\u2010Enhanced Luminescence Thermometry with a Novel Eu3+\/Tb3+ Polymeric Coordination Compound","volume":"9","author":"Polikovskiy T. A.","year":"2025","journal-title":"Materials Chemistry Frontiers"},{"key":"e_1_2_10_27_1","doi-asserted-by":"crossref","first-page":"5354","DOI":"10.3390\/ma17215354","article-title":"Luminescence Thermometry with Eu3+\u2010Doped Y2Mo3O12: Comparison of Performance of Intensity Ratio and Machine Learning Temperature Read\u2010Outs","volume":"17","author":"Gavrilovic T.","year":"2024","journal-title":"Materials"},{"key":"e_1_2_10_28_1","doi-asserted-by":"crossref","first-page":"e0317703","DOI":"10.1371\/journal.pone.0317703","article-title":"Comparative Analysis of Data\u2010Driven Models for Spatially Resolved Thermometry Using Emission Spectroscopy","volume":"20","author":"Kang R.","year":"2025","journal-title":"PLOS ONE"},{"key":"e_1_2_10_29_1","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1364\/OL.507901","article-title":"Luminescence Thermometry Driven by a Support Vector Machine: A Strategy toward Precise Thermal Sensing","volume":"49","author":"Xu W.","year":"2024","journal-title":"Optics Letters"},{"key":"e_1_2_10_30_1","doi-asserted-by":"crossref","first-page":"116550","DOI":"10.1016\/j.sna.2025.116550","article-title":"Correction of Spectral Distortions in Nanothermometry Using Machine Learning","volume":"389","author":"Santos E. H.","year":"2025","journal-title":"Sensors and Actuators A: Physical"},{"key":"e_1_2_10_31_1","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1007\/s10765-023-03277-0","article-title":"Using Recurrent Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data for Use in Bio\u2010Microfluidics","volume":"44","author":"Kullberg J.","year":"2023","journal-title":"International Journal of Thermophysics"},{"key":"e_1_2_10_32_1","doi-asserted-by":"crossref","first-page":"3286","DOI":"10.1038\/s41598-024-52966-9","article-title":"Coupling a Recurrent Neural Network to SPAD TCSPC Systems for Real\u2010Time Fluorescence Lifetime Imaging","volume":"14","author":"Lin Y.","year":"2024","journal-title":"Scientific Reports"},{"key":"e_1_2_10_33_1","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1038\/nnano.2016.111","article-title":"Instantaneous Ballistic Velocity of Suspended Brownian Nanocrystals Measured by Upconversion Nanothermometry","volume":"11","author":"Brites C. D.","year":"2016","journal-title":"Nature Nanotechnology"},{"key":"e_1_2_10_34_1","doi-asserted-by":"crossref","first-page":"24169","DOI":"10.1039\/D0NR06989B","article-title":"Thermal Properties of Lipid Bilayers Derived from the Transient Heating Regime of Upconverting Nanoparticles","volume":"12","author":"Bastos A. R. N.","year":"2020","journal-title":"Nanoscale"},{"key":"e_1_2_10_35_1","doi-asserted-by":"crossref","first-page":"6704","DOI":"10.1021\/acs.jpclett.0c02147","article-title":"Decoding a Percolation Phase Transition of Water at Similar to 330\u2009K with a Nanoparticle Ruler","volume":"11","author":"Brites C. D. S.","year":"2020","journal-title":"The Journal of Physical Chemistry Letters"},{"key":"e_1_2_10_36_1","doi-asserted-by":"crossref","first-page":"2606","DOI":"10.1021\/acs.jpclett.4c00044","article-title":"Deciphering Density Fluctuations in the Hydration Water of Brownian Nanoparticles via Upconversion Thermometry","volume":"15","author":"Maturi F. E.","year":"2024","journal-title":"The Journal of Physical Chemistry Letters"},{"key":"e_1_2_10_37_1","doi-asserted-by":"crossref","first-page":"11683","DOI":"10.1021\/acs.jpclett.5c02981","article-title":"Tuning Water Density Fluctuations with Surface\u2010Charged Colloidal Nanoparticles Probed by Luminescence","volume":"16","author":"Raposo R. S.","year":"2025","journal-title":"The Journal of Physical Chemistry Letters"},{"key":"e_1_2_10_38_1","doi-asserted-by":"crossref","first-page":"12042","DOI":"10.1021\/acs.jpcb.5c06143","article-title":"Exploring Water Beyond the Solvent: Insights into Density Fluctuations and EGFP Unfolding via Luminescence Thermometry","volume":"129","author":"Guo Y. W.","year":"2025","journal-title":"The Journal of Physical Chemistry B"},{"key":"e_1_2_10_39_1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3390\/s23010008","article-title":"Screening of Discrete Wavelet Transform Parameters for the Denoising of Rolling Bearing Signals in Presence of Localised Defects","volume":"23","author":"Brusa E.","year":"2023","journal-title":"Sensors"},{"key":"e_1_2_10_40_1","doi-asserted-by":"crossref","unstructured":"M.Lang H.Guo J. E.Odegard C. S.Burrus andR. O.Wells \u201cNonlinear Processing of a Shift\u2010Invariant Discrete Wavelet Transform (DWT) for Noise Reduction \u201c inWavelet Applications II(1995).","DOI":"10.1117\/12.205427"}],"container-title":["Advanced Intelligent Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/advanced.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/aidi.202600003","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/advanced.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/aidi.202600003","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/advanced.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/aidi.202600003","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T04:50:36Z","timestamp":1774673436000},"score":1,"resource":{"primary":{"URL":"https:\/\/advanced.onlinelibrary.wiley.com\/doi\/10.1002\/aidi.202600003"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,27]]},"references-count":39,"alternative-id":["10.1002\/aidi.202600003"],"URL":"https:\/\/doi.org\/10.1002\/aidi.202600003","archive":["Portico"],"relation":{},"ISSN":["2943-9981","2943-9981"],"issn-type":[{"value":"2943-9981","type":"print"},{"value":"2943-9981","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,27]]},"assertion":[{"value":"2026-01-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e202600003"}}