Optical Properties Prediction for Red and Near-Infrared Emitting Carbon Dots Using Machine Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F24%3A10254764" target="_blank" >RIV/61989100:27740/24:10254764 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989592:15640/24:73625172

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/10.1002/smll.202310402" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/smll.202310402</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/smll.202310402" target="_blank" >10.1002/smll.202310402</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optical Properties Prediction for Red and Near-Infrared Emitting Carbon Dots Using Machine Learning

Popis výsledku v původním jazyce

Functional nanostructures build up a basis for the future materials and devices, providing a wide variety of functionalities, a possibility of designing bio-compatible nanoprobes, etc. However, development of new nanostructured materials via trial-and-error approach is obviously limited by laborious efforts on their syntheses, and the cost of materials and manpower. This is one of the reasons for an increasing interest in design and development of novel materials with required properties assisted by machine learning approaches. Here, the dataset on synthetic parameters and optical properties of one important class of light-emitting nanomaterials - carbon dots are collected, processed, and analyzed with optical transitions in the red and near-infrared spectral ranges. A model for prediction of spectral characteristics of these carbon dots based on multiple linear regression is established and verified by comparison of the predicted and experimentally observed optical properties of carbon dots synthesized in three different laboratories. Based on the analysis, the open-source code is provided to be used by researchers for the prediction of optical properties of carbon dots and their synthetic procedures. The dataset on synthetic parameters and optical properties of red and near-infrared emitting carbon dots are collected, processed, and analyzed. A model for prediction of spectral characteristics of these carbon dots is established as open-source code and experimentally validated in three different laboratories, and it can be accessed by researchers for the prediction of carbon dots properties. image

Název v anglickém jazyce

Optical Properties Prediction for Red and Near-Infrared Emitting Carbon Dots Using Machine Learning

Popis výsledku anglicky

Functional nanostructures build up a basis for the future materials and devices, providing a wide variety of functionalities, a possibility of designing bio-compatible nanoprobes, etc. However, development of new nanostructured materials via trial-and-error approach is obviously limited by laborious efforts on their syntheses, and the cost of materials and manpower. This is one of the reasons for an increasing interest in design and development of novel materials with required properties assisted by machine learning approaches. Here, the dataset on synthetic parameters and optical properties of one important class of light-emitting nanomaterials - carbon dots are collected, processed, and analyzed with optical transitions in the red and near-infrared spectral ranges. A model for prediction of spectral characteristics of these carbon dots based on multiple linear regression is established and verified by comparison of the predicted and experimentally observed optical properties of carbon dots synthesized in three different laboratories. Based on the analysis, the open-source code is provided to be used by researchers for the prediction of optical properties of carbon dots and their synthetic procedures. The dataset on synthetic parameters and optical properties of red and near-infrared emitting carbon dots are collected, processed, and analyzed. A model for prediction of spectral characteristics of these carbon dots is established as open-source code and experimentally validated in three different laboratories, and it can be accessed by researchers for the prediction of carbon dots properties. image

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10400 - Chemical sciences

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Small

ISSN

1613-6810

e-ISSN

1613-6829

Svazek periodika

20

Číslo periodika v rámci svazku

29

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

8

Strana od-do

—

Kód UT WoS článku

001159925500001

EID výsledku v databázi Scopus

2-s2.0-85184488822