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Machine Learning Enabled Image Analysis of Time‐Temperature Sensing Colloidal Arrays

Smart, responsive materials are required in various advanced applications ranging from anti‐counterfeiting to autonomous sensing. Colloidal crystals are a versatile material class for optically based sensing applications owing to their photonic stopband. A careful combination of materials synthesis...

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Autores principales: Schöttle, Marius, Tran, Thomas, Oberhofer, Harald, Retsch, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10015860/
https://www.ncbi.nlm.nih.gov/pubmed/36670061
http://dx.doi.org/10.1002/advs.202205512
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author Schöttle, Marius
Tran, Thomas
Oberhofer, Harald
Retsch, Markus
author_facet Schöttle, Marius
Tran, Thomas
Oberhofer, Harald
Retsch, Markus
author_sort Schöttle, Marius
collection PubMed
description Smart, responsive materials are required in various advanced applications ranging from anti‐counterfeiting to autonomous sensing. Colloidal crystals are a versatile material class for optically based sensing applications owing to their photonic stopband. A careful combination of materials synthesis and colloidal mesostructure rendered such systems helpful in responding to stimuli such as gases, humidity, or temperature. Here, an approach is demonstrated to simultaneously and independently measure the time and temperature solely based on the inherent material properties of complex colloidal crystal mixtures. An array of colloidal crystals, each featuring unique film formation kinetics, is fabricated. Combined with machine learning‐enabled image analysis, the colloidal crystal arrays can autonomously record isothermal heating events — readout proceeds by acquiring photographs of the applied sensor using a standard smartphone camera. The concept shows how the progressing use of machine learning in materials science has the potential to allow non‐classical forms of data acquisition and evaluation. This can provide novel insights into multiparameter systems and simplify applications of novel materials.
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spelling pubmed-100158602023-03-16 Machine Learning Enabled Image Analysis of Time‐Temperature Sensing Colloidal Arrays Schöttle, Marius Tran, Thomas Oberhofer, Harald Retsch, Markus Adv Sci (Weinh) Research Articles Smart, responsive materials are required in various advanced applications ranging from anti‐counterfeiting to autonomous sensing. Colloidal crystals are a versatile material class for optically based sensing applications owing to their photonic stopband. A careful combination of materials synthesis and colloidal mesostructure rendered such systems helpful in responding to stimuli such as gases, humidity, or temperature. Here, an approach is demonstrated to simultaneously and independently measure the time and temperature solely based on the inherent material properties of complex colloidal crystal mixtures. An array of colloidal crystals, each featuring unique film formation kinetics, is fabricated. Combined with machine learning‐enabled image analysis, the colloidal crystal arrays can autonomously record isothermal heating events — readout proceeds by acquiring photographs of the applied sensor using a standard smartphone camera. The concept shows how the progressing use of machine learning in materials science has the potential to allow non‐classical forms of data acquisition and evaluation. This can provide novel insights into multiparameter systems and simplify applications of novel materials. John Wiley and Sons Inc. 2023-01-20 /pmc/articles/PMC10015860/ /pubmed/36670061 http://dx.doi.org/10.1002/advs.202205512 Text en © 2023 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Schöttle, Marius
Tran, Thomas
Oberhofer, Harald
Retsch, Markus
Machine Learning Enabled Image Analysis of Time‐Temperature Sensing Colloidal Arrays
title Machine Learning Enabled Image Analysis of Time‐Temperature Sensing Colloidal Arrays
title_full Machine Learning Enabled Image Analysis of Time‐Temperature Sensing Colloidal Arrays
title_fullStr Machine Learning Enabled Image Analysis of Time‐Temperature Sensing Colloidal Arrays
title_full_unstemmed Machine Learning Enabled Image Analysis of Time‐Temperature Sensing Colloidal Arrays
title_short Machine Learning Enabled Image Analysis of Time‐Temperature Sensing Colloidal Arrays
title_sort machine learning enabled image analysis of time‐temperature sensing colloidal arrays
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10015860/
https://www.ncbi.nlm.nih.gov/pubmed/36670061
http://dx.doi.org/10.1002/advs.202205512
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