Understanding the chemistry of customized carbonaceous nanomaterials

Anja Kristiina Aarva

Understanding the chemistry of customized carbonaceous nanomaterials

Julkaisun otsikon käännös: Hiilipohjaisten nanomateriaalien pintakemian syvällinen analyysi

Anja Kristiina Aarva

Tutkimustuotos: Doctoral Thesis › Collection of Articles

Abstrakti

Hiileen pohjautuvilla nanomateriaaleilla on lukuisia ominaisuuksia, jotka tekevät niistä kiinnostavia tutkimuskohteita useilla tieteen aloilla. Mahdollisia sovellutuskohteita löytyy esimerkiksi energiaratkaisuiden, pinnoitteiden ja sähkökemiallisten antureiden parista. Tässä työssä tutkitaan näiden materiaalien rakenteen ja ominaisuuksien välistä suhdetta laskennallisia menetelmiä hyödyntäen. Tarkastelun kohteena on erityisesti hiilen pintakemia. Työssä on kehitetty uusi menetelmä hiilipohjaisten materiaalien karakterisointia varten. Menetelmä perustuu kokeellisten tulosten systemaattiseen analyysiin laskennallisten tulosten avulla. Tarkastelun kohteina olevat materiaalit sisältävät usein hiilen lisäksi myös muita alkuaineita, kuten vetyä ja happea. Koska materiaalien ominaisuudet riippuvat niiden kemiallisesta koostumuksesta, sisältää tämä työ myös yksityiskohtaisen analyysin hiilipintojen reaktiivisuudesta. Analyysi on suoritettu yhdistämällä tiheysfunktionaaliteoriaan sekä koneoppimiseen perustuvia menetelmiä. Koneoppisen avulla saavutetaan tarvittava määrä tutkittavia rakenteita, jotta tulokset olisivat tilastollisesti merkittäviä, sekä rakenteiden merkityksellinen luokittelu, kun taas tiheysfunktionaaliteoria tarjoaa laskennallista tarkkuutta – menetelmät siis tukevat toisiaan. Kun olemme tarkastelleet hiilipintojen reaktiivisuutta, siirrymme materiaalin karakterisointiin. Tämä on välttämätöntä, jotta laskennallisia ja kokeellisia tuloksia voidaan vertailla. Vertailu on arvokasta kahdella tapaa: haluamme ymmärtää kokeellisia tuloksia paremmin, mutta samalla myös varmistaa, että laskennalliset mallit kuvaavat todellisuutta mahdollisimman todenmukaisesti. Toisin sanoen, tarkistamme, että mallimme mahdollistavat laskennallisen kokeen. Röntgenspektroskopiaan perustuvilla mittauksilla on mahdollista analysoida materiaalien pintakemiaa erittäin tarkasti. Mutta, erityisesti, jos näytteen rakenne on järjestäytymätön, kuten amorfisen hiilen tapauksessa, tulosten tulkinta voi olla hyvin haastavaa. Tässä työssä esitelty laskennallinen menetelmä tarjoaa uuden työkalun tulosten tulkintaan ja mahdollistaa tulosten tulkinnan atomitasolla. Loppupäätelmänä voidaan todeta, että laskennallisten menetelmien yhdistäminen kokeellisten tulosten tulkitsemiseksi tukee uusien räätälöityjen nanomateriaalien kehitystyötä. Näille materiaaleille on kysyntää esimerkiksi sähkökemiallisten antureiden suunnittelussa, mutta myös lukuisilla muilla korkean teknologian aloilla.

Julkaisun otsikon käännös	Hiilipohjaisten nanomateriaalien pintakemian syvällinen analyysi
Alkuperäiskieli	Englanti
Pätevyys	Tohtorintutkinto
Myöntävä instituutio	Aalto-yliopisto
Valvoja/neuvonantaja	Laurila, Tomi, Vastuuprofessori Caro Bayo, Miguel, Ohjaaja
Kustantaja	Aalto University
Painoksen ISBN	978-952-64-1034-0
Sähköinen ISBN	978-952-64-1035-7
Tila	Julkaistu - 2022
OKM-julkaisutyyppi	G5 Artikkeliväitöskirja

Tutkimusalat

nanomateriaalit
hiili
sähkökemia
tiheysfunktionaaliteoria
koneoppiminen

Pääsy asiakirjaan

http://urn.fi/URN:ISBN:978-952-64-1035-7

Science-IT
Hakala, M. (Manager)
Perustieteiden korkeakoulu
Laitteistot/tilat: Facility

Siteeraa tätä

@phdthesis{31d467d919904d41b929d50d7d2d5ea6,

title = "Understanding the chemistry of customized carbonaceous nanomaterials",

abstract = "Carbon-based nanomaterials have exceptional properties, that can lead to new advanced applications in many fields of technology and science, for instance: energy solutions, protective and biomedical coatings, and biosensing. This thesis is focused on computational methods used to understand better the structure-property relationships of these materials. We look deeply into surface chemistry of carbon nanomaterials and develop a new method for characterization of carbonaceous materials by combining computational and experimental data. In addition to elemental carbon, carbonaceous nanomaterials often contain other elements, like hydrogen and oxygen. As the properties of the materials depend on their chemical composition we carried out a detailed analysis about reactivity of amorphous carbon surfaces to obtain more information on this connection. In this work, a combination of novel computational methods were employed: density functional theory-level calculations, with machine learning-based structure construction and classification of the chemical environments present in the structures. This approach enables us to achieve statistically meaningful data-set, with high accuracy and low computational cost. Once we have studied the reactivity of carbon, careful material characterization is in place, so that it is possible to compare computational results with experimental samples. This comparison is valuable in two ways: we want to understand the experimental data better but, on the other hand, there is a need to make sure that the computational samples reflect experimental results in a realistic way. In other words, our models enable computational experiments. X-ray spectroscopy provides an extremely accurate means for analyzing the surface chemistry of substances in general. However, interpretation of the experimental data, especially when the sample is disordered, can prove to be challenging. The computational method that we introduce in this thesis can recreate the X-ray spectra from first principles, which provides a long searched connection between macroscopic and atomic level structures. As a conclusion, the information gained by combining novel computational methods with experimental science can be used to develop customized materials, e.g., for electrochemical detection of dopamine, which is used as a model system in this study, but also for several other promising applications.",

keywords = "nanomaterials, carbon, electrochemistry, density functional theory, machine learning, nanomateriaalit, hiili, s{\"a}hk{\"o}kemia, tiheysfunktionaaliteoria, koneoppiminen, nanomaterials, carbon, electrochemistry, density functional theory, machine learning",

author = "Aarva, {Anja Kristiina}",

year = "2022",

language = "English",

isbn = "978-952-64-1034-0",

series = "Aalto University publication series DOCTORAL THESES",

publisher = "Aalto University",

number = "174",

address = "Finland",

school = "Aalto University",

}

TY - BOOK

T1 - Understanding the chemistry of customized carbonaceous nanomaterials

AU - Aarva, Anja Kristiina

PY - 2022

Y1 - 2022

N2 - Carbon-based nanomaterials have exceptional properties, that can lead to new advanced applications in many fields of technology and science, for instance: energy solutions, protective and biomedical coatings, and biosensing. This thesis is focused on computational methods used to understand better the structure-property relationships of these materials. We look deeply into surface chemistry of carbon nanomaterials and develop a new method for characterization of carbonaceous materials by combining computational and experimental data. In addition to elemental carbon, carbonaceous nanomaterials often contain other elements, like hydrogen and oxygen. As the properties of the materials depend on their chemical composition we carried out a detailed analysis about reactivity of amorphous carbon surfaces to obtain more information on this connection. In this work, a combination of novel computational methods were employed: density functional theory-level calculations, with machine learning-based structure construction and classification of the chemical environments present in the structures. This approach enables us to achieve statistically meaningful data-set, with high accuracy and low computational cost. Once we have studied the reactivity of carbon, careful material characterization is in place, so that it is possible to compare computational results with experimental samples. This comparison is valuable in two ways: we want to understand the experimental data better but, on the other hand, there is a need to make sure that the computational samples reflect experimental results in a realistic way. In other words, our models enable computational experiments. X-ray spectroscopy provides an extremely accurate means for analyzing the surface chemistry of substances in general. However, interpretation of the experimental data, especially when the sample is disordered, can prove to be challenging. The computational method that we introduce in this thesis can recreate the X-ray spectra from first principles, which provides a long searched connection between macroscopic and atomic level structures. As a conclusion, the information gained by combining novel computational methods with experimental science can be used to develop customized materials, e.g., for electrochemical detection of dopamine, which is used as a model system in this study, but also for several other promising applications.

AB - Carbon-based nanomaterials have exceptional properties, that can lead to new advanced applications in many fields of technology and science, for instance: energy solutions, protective and biomedical coatings, and biosensing. This thesis is focused on computational methods used to understand better the structure-property relationships of these materials. We look deeply into surface chemistry of carbon nanomaterials and develop a new method for characterization of carbonaceous materials by combining computational and experimental data. In addition to elemental carbon, carbonaceous nanomaterials often contain other elements, like hydrogen and oxygen. As the properties of the materials depend on their chemical composition we carried out a detailed analysis about reactivity of amorphous carbon surfaces to obtain more information on this connection. In this work, a combination of novel computational methods were employed: density functional theory-level calculations, with machine learning-based structure construction and classification of the chemical environments present in the structures. This approach enables us to achieve statistically meaningful data-set, with high accuracy and low computational cost. Once we have studied the reactivity of carbon, careful material characterization is in place, so that it is possible to compare computational results with experimental samples. This comparison is valuable in two ways: we want to understand the experimental data better but, on the other hand, there is a need to make sure that the computational samples reflect experimental results in a realistic way. In other words, our models enable computational experiments. X-ray spectroscopy provides an extremely accurate means for analyzing the surface chemistry of substances in general. However, interpretation of the experimental data, especially when the sample is disordered, can prove to be challenging. The computational method that we introduce in this thesis can recreate the X-ray spectra from first principles, which provides a long searched connection between macroscopic and atomic level structures. As a conclusion, the information gained by combining novel computational methods with experimental science can be used to develop customized materials, e.g., for electrochemical detection of dopamine, which is used as a model system in this study, but also for several other promising applications.

KW - nanomaterials

KW - carbon

KW - electrochemistry

KW - density functional theory

KW - machine learning

KW - nanomateriaalit

KW - hiili

KW - sähkökemia

KW - tiheysfunktionaaliteoria

KW - koneoppiminen

KW - nanomaterials

KW - carbon

KW - electrochemistry

KW - density functional theory

KW - machine learning

M3 - Doctoral Thesis

SN - 978-952-64-1034-0

T3 - Aalto University publication series DOCTORAL THESES

PB - Aalto University

ER -

Understanding the chemistry of customized carbonaceous nanomaterials

Abstrakti

Tutkimusalat

Pääsy asiakirjaan

Sormenjälki

Laitteet

Science-IT

Siteeraa tätä