Cargando…

XAS Data Preprocessing of Nanocatalysts for Machine Learning Applications

Innovative development in the energy and chemical industries is mainly dependent on advances in the accelerated design and development of new functional materials. The success of research in new nanocatalysts mainly relies on modern techniques and approaches for their precise characterization. The e...

Descripción completa

Detalles Bibliográficos
Autores principales:	Kartashov, Oleg O., Chernov, Andrey V., Polyanichenko, Dmitry S., Butakova, Maria A.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2021
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709119/ https://www.ncbi.nlm.nih.gov/pubmed/34947477 http://dx.doi.org/10.3390/ma14247884

Ejemplares similares

Deep Reinforcement Learning Environment Approach Based on Nanocatalyst XAS Diagnostics Graphic Formalization
por: Polyanichenko, Dmitry S., et al.
Publicado: (2023)

Machine Learning and 3D Reconstruction of Materials Surface for Nondestructive Inspection
por: Kartashov, Oleg O., et al.
Publicado: (2022)

Integrated Video and Acoustic Emission Data Fusion for Intelligent Decision Making in Material Surface Inspection System
por: Chernov, Andrey V., et al.
Publicado: (2022)

Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials
por: Butakova, Maria A., et al.
Publicado: (2021)

Overview of data preprocessing for machine learning applications in human microbiome research
por: Ibrahimi, Eliana, et al.
Publicado: (2023)

Microbiome Preprocessing Machine Learning Pipeline
por: Jasner, Yoel, et al.
Publicado: (2021)

Imbalanced data preprocessing techniques for machine learning: a systematic mapping study
por: Werner de Vargas, Vitor, et al.
Publicado: (2022)

Omics Data Preprocessing for Machine Learning: A Case Study in Childhood Obesity
por: Torres-Martos, Álvaro, et al.
Publicado: (2023)

Machine learning with Python cookbook: practical solutions from preprocessing to deep learning
por: Albon, Chris
Publicado: (2018)

A TeXas style introduction to proof
por: Taylor, Ron, et al.
Publicado: (2017)

Data preprocessing in data mining
por: García, Salvador, et al.
Publicado: (2015)

Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning
por: Burdack, Johannes, et al.
Publicado: (2020)

Fully automated beamline control system for XAS beamlines
por: Mangold, Stefan
Publicado: (2018)

Probing calcium solvation by XAS, MD and DFT calculations
por: Yang, Feipeng, et al.
Publicado: (2020)

Use of XAS for the elucidation of metal structure and function: applications to nickel biochemistry, molecular toxicology, and carcinogenesis.
por: Carrington, Paul E, et al.
Publicado: (2002)

automRm: An R Package for Fully Automatic LC-QQQ-MS Data Preprocessing Powered by Machine Learning
por: Eilertz, Daniel, et al.
Publicado: (2022)

Machine learning, waveform preprocessing and feature extraction methods for classification of acoustic startle waveforms
por: Fawcett, Timothy J., et al.
Publicado: (2020)

Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease
por: Md, Abdul Quadir, et al.
Publicado: (2023)

High-resolution XAS/XES: analyzing electronic structures of catalysts
por: Sa, Jacinto
Publicado: (2014)

Molybdenum L-Edge XAS Spectra of MoFe Nitrogenase
por: Bjornsson, Ragnar, et al.
Publicado: (2015)

An In Situ XAS Study of the Cobalt Rhenium Catalyst for Ammonia Synthesis
por: Mathisen, Karina, et al.
Publicado: (2018)

A Comprehensive Evaluation of Metabolomics Data Preprocessing Methods for Deep Learning
por: Abram, Krzysztof Jan, et al.
Publicado: (2022)

Data Preprocessing with Python for Absolute Beginners
por: OU, AI
Publicado: (2021)

A Preprocessing Perspective for Quantum Machine Learning Classification Advantage in Finance Using NISQ Algorithms
por: Mancilla, Javier, et al.
Publicado: (2022)

A Low Complexity Rolling Bearing Diagnosis Technique Based on Machine Learning and Smart Preprocessing
por: Fort, Ada, et al.
Publicado: (2023)

Deep learning for tipping points: Preprocessing matters
por: Dablander, Fabian, et al.
Publicado: (2022)

Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions
por: Soldatov, Mikhail A., et al.
Publicado: (2021)

Electrochemical Preparation of Nanocatalysts and Their Application in Electrocatalysis
por: Zhou, Hao
Publicado: (2022)

XAS/DRIFTS/MS spectroscopy for time-resolved operando investigations at high temperature
por: Agostini, G., et al.
Publicado: (2018)

In situ synchrotron XAS study of the decomposition kinetics of ZDDP triboreactive interfaces
por: Dorgham, Abdel, et al.
Publicado: (2018)

Preprocessing differential methylation hybridization microarray data
por: Sun, Shuying, et al.
Publicado: (2011)

Feature Detection Techniques for Preprocessing Proteomic Data
por: Sellers, Kimberly F., et al.
Publicado: (2010)

Data Preprocessing and Model Design for Medicine Problems
por: Guillén, Alberto, et al.
Publicado: (2013)

Context-based preprocessing of molecular docking data
por: Winck, Ana T, et al.
Publicado: (2013)

ASAP: an environment for automated preprocessing of sequencing data
por: Torstenson, Eric S, et al.
Publicado: (2013)

Optimization of miRNA-seq data preprocessing
por: Tam, Shirley, et al.
Publicado: (2015)

Preprocessing pupil size data: Guidelines and code
por: Kret, Mariska E., et al.
Publicado: (2018)

Using Entropy in Web Usage Data Preprocessing
por: Munk, Michal, et al.
Publicado: (2018)

Universal preprocessing of single-cell genomics data
por: Booeshaghi, A. Sina, et al.
Publicado: (2023)

Evaluation of a machine learning algorithms for predicting the dental age of adolescent based on different preprocessing methods
por: Shen, Shihui, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_gata9o4ga7v58eiogpkjkndhtt