A Neural Network to Decipher Organic Electrochemical Transistors’ Multivariate Responses for Cation Recognition
Extracting relevant data from real-world experiments is often challenging with intrinsic materials and device property dispersion, such as in organic electronics. However, multivariate data analysis can often be a mean to circumvent this and to extract more information when larger datasets are used...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Electronic Materials |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-3978/4/2/7 |
| _version_ | 1797595179321917440 |
|---|---|
| author | Sébastien Pecqueur Dominique Vuillaume Željko Crljen Ivor Lončarić Vinko Zlatić |
| author_facet | Sébastien Pecqueur Dominique Vuillaume Željko Crljen Ivor Lončarić Vinko Zlatić |
| author_sort | Sébastien Pecqueur |
| collection | DOAJ |
| description | Extracting relevant data from real-world experiments is often challenging with intrinsic materials and device property dispersion, such as in organic electronics. However, multivariate data analysis can often be a mean to circumvent this and to extract more information when larger datasets are used with learning algorithms instead of physical models. Here, we report on identifying relevant information descriptors for organic electrochemical transistors (OECTs) to classify aqueous electrolytes by ionic composition. Applying periodical gate pulses at different voltage magnitudes, we extracted a reduced number of nonredundant descriptors from the rich drain-current dynamics, which provide enough information to cluster electrochemical data by principal component analysis between Ca<sup>2+</sup>-, K<sup>+</sup>-, and Na<sup>+</sup>-rich electrolytes. With six current values obtained at the appropriate time domain of the device charge/discharge transient, one can identify the cationic identity of a locally probed transient current with only a single micrometric device. Applied to OECT-based neural sensors, this analysis demonstrates the capability for a single nonselective device to retrieve the rich ionic identity of neural activity at the scale of each neuron individually when learning algorithms are applied to the device physics. |
| first_indexed | 2024-03-11T02:32:59Z |
| format | Article |
| id | doaj.art-b54d3ca59caa43108a4489911373266d |
| institution | Directory Open Access Journal |
| issn | 2673-3978 |
| language | English |
| last_indexed | 2024-03-11T02:32:59Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronic Materials |
| spelling | doaj.art-b54d3ca59caa43108a4489911373266d2023-11-18T10:07:22ZengMDPI AGElectronic Materials2673-39782023-05-0142809410.3390/electronicmat4020007A Neural Network to Decipher Organic Electrochemical Transistors’ Multivariate Responses for Cation RecognitionSébastien Pecqueur0Dominique Vuillaume1Željko Crljen2Ivor Lončarić3Vinko Zlatić4Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN, F-59000 Lille, FranceUniv. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN, F-59000 Lille, FranceRuđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, CroatiaRuđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, CroatiaRuđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, CroatiaExtracting relevant data from real-world experiments is often challenging with intrinsic materials and device property dispersion, such as in organic electronics. However, multivariate data analysis can often be a mean to circumvent this and to extract more information when larger datasets are used with learning algorithms instead of physical models. Here, we report on identifying relevant information descriptors for organic electrochemical transistors (OECTs) to classify aqueous electrolytes by ionic composition. Applying periodical gate pulses at different voltage magnitudes, we extracted a reduced number of nonredundant descriptors from the rich drain-current dynamics, which provide enough information to cluster electrochemical data by principal component analysis between Ca<sup>2+</sup>-, K<sup>+</sup>-, and Na<sup>+</sup>-rich electrolytes. With six current values obtained at the appropriate time domain of the device charge/discharge transient, one can identify the cationic identity of a locally probed transient current with only a single micrometric device. Applied to OECT-based neural sensors, this analysis demonstrates the capability for a single nonselective device to retrieve the rich ionic identity of neural activity at the scale of each neuron individually when learning algorithms are applied to the device physics.https://www.mdpi.com/2673-3978/4/2/7organic electrochemical transistorprincipal component analysisneural networkion sensingdynamic analysis |
| spellingShingle | Sébastien Pecqueur Dominique Vuillaume Željko Crljen Ivor Lončarić Vinko Zlatić A Neural Network to Decipher Organic Electrochemical Transistors’ Multivariate Responses for Cation Recognition Electronic Materials organic electrochemical transistor principal component analysis neural network ion sensing dynamic analysis |
| title | A Neural Network to Decipher Organic Electrochemical Transistors’ Multivariate Responses for Cation Recognition |
| title_full | A Neural Network to Decipher Organic Electrochemical Transistors’ Multivariate Responses for Cation Recognition |
| title_fullStr | A Neural Network to Decipher Organic Electrochemical Transistors’ Multivariate Responses for Cation Recognition |
| title_full_unstemmed | A Neural Network to Decipher Organic Electrochemical Transistors’ Multivariate Responses for Cation Recognition |
| title_short | A Neural Network to Decipher Organic Electrochemical Transistors’ Multivariate Responses for Cation Recognition |
| title_sort | neural network to decipher organic electrochemical transistors multivariate responses for cation recognition |
| topic | organic electrochemical transistor principal component analysis neural network ion sensing dynamic analysis |
| url | https://www.mdpi.com/2673-3978/4/2/7 |
| work_keys_str_mv | AT sebastienpecqueur aneuralnetworktodecipherorganicelectrochemicaltransistorsmultivariateresponsesforcationrecognition AT dominiquevuillaume aneuralnetworktodecipherorganicelectrochemicaltransistorsmultivariateresponsesforcationrecognition AT zeljkocrljen aneuralnetworktodecipherorganicelectrochemicaltransistorsmultivariateresponsesforcationrecognition AT ivorloncaric aneuralnetworktodecipherorganicelectrochemicaltransistorsmultivariateresponsesforcationrecognition AT vinkozlatic aneuralnetworktodecipherorganicelectrochemicaltransistorsmultivariateresponsesforcationrecognition AT sebastienpecqueur neuralnetworktodecipherorganicelectrochemicaltransistorsmultivariateresponsesforcationrecognition AT dominiquevuillaume neuralnetworktodecipherorganicelectrochemicaltransistorsmultivariateresponsesforcationrecognition AT zeljkocrljen neuralnetworktodecipherorganicelectrochemicaltransistorsmultivariateresponsesforcationrecognition AT ivorloncaric neuralnetworktodecipherorganicelectrochemicaltransistorsmultivariateresponsesforcationrecognition AT vinkozlatic neuralnetworktodecipherorganicelectrochemicaltransistorsmultivariateresponsesforcationrecognition |