Results 21 to 30 of about 6,116,936 (306)

Sublimable materials facilitate the TEM sample preparation of oil-soluble nanomaterials

open access: yesApplied Microscopy, 2020
Sample preparation is significantly important to the high-resolution transmission electron microscopy (HRTEM) characterization of nanomaterials. However, many general organic solvents can dissolve the necessary organic polymer support layer in TEM grid ...
Yu-Hao Deng
doaj   +1 more source

Optical Characterization of ALD-Coated Nanoporous Alumina Structures: Effect of Sample Geometry or Coated Layer Material

open access: yesMicromachines, 2023
Optical characterization of nanoporous alumina-based structures (NPA-bSs), obtained by ALD deposition of a thin conformal SiO2 layer on two alumina nanosupports with different geometrical parameters (pore size and interpore distance), was performed by ...
Ana Laura Cuevas   +5 more
doaj   +1 more source

Validation of data analysis routines for a thermal probe apparatus using numerical data sets [PDF]

open access: yes, 2008
Most thermal properties of construction materials used in the analysis of building performance have been measured under laboratory conditions, using a guarded hot box or hot plate apparatus. As a consequence, these properties seldom reflect the impact of
De, Wilde, Goodhew, S, Griffiths, R
core   +1 more source

Interference of the Spectral Line Mζ in the M Energy Level Series of Ce on the F Element Kα Peak in Monazite Samples

open access: yesYankuang ceshi
Quantitative analysis of the ultra-light element fluorine (F) has always been one of the difficulties in electron probe microanalysis (EPMA). High-resolution qualitative analysis and proper subtraction of spectral interferences from major elements are ...
Suwen QIU   +5 more
doaj   +1 more source

RANdom SAmple Consensus (RANSAC) algorithm for material-informatics: application to photovoltaic solar cells

open access: yesJournal of Cheminformatics, 2017
An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RANSAC) algorithm is a predictive modeling tool
Omer Kaspi   +2 more
doaj   +1 more source

INNOVATIVE PROCESS LINE FOR MATERIAL SAMPLE PREPARATION

open access: yesSustainable Extraction and Processing of Raw Materials Journal
. The successful establishment and sustainable development of mining enterprises depends on a number of factors, including geological research, management of mineral extraction and processing processes, and sample preparation technology.
O. V. Fedoskina   +4 more
doaj   +1 more source

Modern Methods of Pre-Treatment of Plant Material for the Extraction of Bioactive Compounds

open access: yesMolecules, 2022
In this review, recent advances in the methods of pre-treatment of plant material for the extraction of secondary metabolites with high biological activity are presented.
Aneta Krakowska-Sieprawska   +4 more
doaj   +1 more source

Development of biodegradable composite micro-perforated panel made from natural fibre composites with evaluation of its acoustic and mechanical properties [PDF]

open access: yes, 2020
Micro-perforated panel (MPP) has been widely considered as a promising alternative for sound absorption purposes. Plenty of research has been done to improve the sound absorption of MPP but no specific work highlights the material structure effect ...
Chin, Desmond Daniel Vui Sheng
core  

New double indentation technique for measurement of the elasticity modulus of thin objects [PDF]

open access: yes, 2011
In this paper we introduce a new method to determine the Young's modulus of thin (biological) samples. The method is especially suitable for small objects with a thickness of a few hundred micrometers.
De Baere, Ives   +2 more
core   +2 more sources

Charge balance calculations for mixed salt systems applied to a large dataset from the built environment

open access: yesScientific Data, 2022
Measurement(s) Ion • inorganic salt Technology Type(s) ion chromatography Factor Type(s) sample location country code (SLCC) • sample city (SC) • sample (site) name • sample material (SM) • sample height (H) • Sample depth from surface of specific ...
Sebastiaan Godts   +6 more
doaj   +1 more source

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