Improving the Accuracy of Carbon Dot Temperature Sensing Using Multi-Dimensional Machine Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F24%3A10254106" target="_blank" >RIV/61989100:27740/24:10254106 - isvavai.cz</a>

Výsledek na webu

<a href="https://pubs.acs.org/doi/10.1021/acsanm.3c05688" target="_blank" >https://pubs.acs.org/doi/10.1021/acsanm.3c05688</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1021/acsanm.3c05688" target="_blank" >10.1021/acsanm.3c05688</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving the Accuracy of Carbon Dot Temperature Sensing Using Multi-Dimensional Machine Learning

Popis výsledku v původním jazyce

Optical sensing methods offer a convenient noncontact approach to monitor different environmental parameters with a high spatial resolution and fast response times. Temperature monitoring can benefit from optical sensing using luminescent nanoprobes, but many of those substances are toxic or expensive. Carbon dots are a class of luminescent colloidal nanoparticles that have recently gained recognition as optical probes, which are easy to produce by environmentally friendly synthesis, nontoxic, and stable. While carbon dots show temperature-dependent optical properties, their broad emission profiles may constitute a challenge for optical sensing. In this study, three types of carbon dots with different emission profiles were tested as optical probes for intensity-, spectral-shift-, intensity-ratio-, bandwidth-, and lifetime-based temperature sensing. Depending on the optical characteristics of the specific probe, either intensity- or lifetime-based sensing was shown to be the most accurate, with accuracies of up to 1.65 and 0.70 K, respectively. Employing Gaussian fits improved accuracies of the intensity-ratio-based sensing to 1.24 K, with the additional benefit of greater stability against excitation fluctuations. Finally, a multiple linear regression model combining steady-state and time-resolved luminescence data of carbon dots has been applied to further increase the sensing accuracies with carbon dots to 0.54 K. Our study demonstrates how multidimensional machine learning methods can greatly improve temperature sensing with optical probes. (C) 2024 American Chemical Society.

Název v anglickém jazyce

Improving the Accuracy of Carbon Dot Temperature Sensing Using Multi-Dimensional Machine Learning

Popis výsledku anglicky

Optical sensing methods offer a convenient noncontact approach to monitor different environmental parameters with a high spatial resolution and fast response times. Temperature monitoring can benefit from optical sensing using luminescent nanoprobes, but many of those substances are toxic or expensive. Carbon dots are a class of luminescent colloidal nanoparticles that have recently gained recognition as optical probes, which are easy to produce by environmentally friendly synthesis, nontoxic, and stable. While carbon dots show temperature-dependent optical properties, their broad emission profiles may constitute a challenge for optical sensing. In this study, three types of carbon dots with different emission profiles were tested as optical probes for intensity-, spectral-shift-, intensity-ratio-, bandwidth-, and lifetime-based temperature sensing. Depending on the optical characteristics of the specific probe, either intensity- or lifetime-based sensing was shown to be the most accurate, with accuracies of up to 1.65 and 0.70 K, respectively. Employing Gaussian fits improved accuracies of the intensity-ratio-based sensing to 1.24 K, with the additional benefit of greater stability against excitation fluctuations. Finally, a multiple linear regression model combining steady-state and time-resolved luminescence data of carbon dots has been applied to further increase the sensing accuracies with carbon dots to 0.54 K. Our study demonstrates how multidimensional machine learning methods can greatly improve temperature sensing with optical probes. (C) 2024 American Chemical Society.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

21001 - Nano-materials (production and properties)

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ACS Applied Nano Materials

ISSN

2574-0970

e-ISSN

—

Svazek periodika

7

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

2258-2269

Kód UT WoS článku

001152654200001

EID výsledku v databázi Scopus

2-s2.0-85182014288