scholarly journals Controllable synthesization of Au nanoparticles by laser enhanced wet KOH etching process

2021 ◽  
Vol 1963 (1) ◽  
pp. 012009
Author(s):  
Alwan M Alwan ◽  
Ali A. Youssef ◽  
Aseel A. Chasb
Keyword(s):  
Au Nanoparticles ◽  
Etching Process ◽  
Koh Etching

BPD-TED Conversion in the SiC Substrate after High-Temperature Si-VE

2020 ◽  
Vol 1004 ◽  
pp. 401-407
Author(s):  
Yusuke Sudoh ◽  
Makoto Kitabatake ◽  
Tadaaki Kaneko
Keyword(s):  
Silicon Carbide ◽  
High Temperature ◽  
Chemical Etching ◽  
Surface Region ◽  
Etching Process ◽  
Koh Etching

We propose the Si-Vapor Etching (Si-VE), which is thermal chemical etching process, as epi-ready treatment for Silicon Carbide (SiC). In this work, we report the evaluation results of BPD-TED conversion by Si-VE treatment using repeated KOH etching process. This method makes it possible to observe BPD-TED conversion in a very shallow surface region of the SiC substrate. 80% of BPDs is converted to TEDs with a depth of more than 80nm under optimized Si-VE 2000°C conditions. Furthermore, 53% of BPDs were converted to TEDs with 140nm or more depth, which has been confirmed under optimized 1800°C Si-VE conditions.

scholarly journals Optimization of KOH etching process for MEMS square diaphragm using response surface method

2019 ◽  
Vol 15 (1) ◽  
pp. 113 ◽  
Author(s):  
Norliana Yusof ◽  
Badariah Bais ◽  
Burhanuddin Yeop Majlis ◽  
Norhayati Soin ◽  
Jumril Yunas
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Process Parameters ◽  
Numerical Study ◽  
Etching Rate ◽  
Wet Etching ◽  
Etching Process ◽  
Etching Time ◽  
Surface Method ◽  
Koh Etching

<em><span>KOH wet etching is widely used in realizing MEMS diaphragm due to its low cost, safe and easy handling. However, wet etching process parameters need to be studied thoroughly in order to realize the desired shape and size of MEMS devices. This paper presents the numerical study and optimization of KOH etching process parameters using the response surface method (RSM). Face central composite design (FCC) of RSM was employed as the experimental design to analyze the result and generate a mathematical prediction model. From the analysis, the temperature was identified as the most significant process parameter that affects the etching rate, thus affecting the thickness and size of the diaphragm. The results of RSM prediction for optimization were applied in this study. Particularly, 45% of KOH concentration, temperature of 80°C, 1735 µm2 of mask size, and 7.2 hours of etching time were implemented to obtain a square MEMS diaphragm with thickness of 120 µm and size of 1200 µm2. The results of RSM based optimization method for KOH wet etching offers a quick and effective method for realizing a desired MEMS device.</span></em>

Optical Properties of Black Silicon Using the Combination Method of KOH and Gold-Assisted HF Etching

2014 ◽  
Vol 918 ◽  
pp. 42-46
Author(s):  
Guo Dong Zhao ◽  
Xing Hua Zhu ◽  
You Yu ◽  
Xiao Lin Zheng
Keyword(s):  
Optical Properties ◽  
Wavelength Range ◽  
Au Nanoparticles ◽  
Combination Method ◽  
Wave Number ◽  
Black Silicon ◽  
Silicon Sample ◽  
Additional Absorption ◽  
Micro Scale ◽  
Koh Etching

A kind of hill-like black silicon have been designed and fabricated by using the combination method of KOH anisotropic etching and gold-assisted HF etching. Pillars array on the surface of a silicon sample was obtained by KOH etching with a SiNx film dots pattern used as a mask. The sample was then etched in the oxidant HF solution catalyzed by Au nanoparticles for 5 minutes. The etched sample appears dark black. This black silicon is orderly hill-like textures in micro-scale with density nanopores on them. It can suppress the reflection to less than 4% in wavelength range from 250nm to 1000nm, and to about 2.5% at the wave number of 2000-4000 cm-1. It also has high additional absorption in IR range.

scholarly journals Optimization of KOH etching process to obtain textured substrates suitable for heterojunction solar cells fabricated by HWCVD

2009 ◽  
Vol 517 (12) ◽  
pp. 3578-3580 ◽  
Author(s):  
D. Muñoz ◽  
P. Carreras ◽  
J. Escarré ◽  
D. Ibarz ◽  
S. Martín de Nicolás ◽  
...  
Keyword(s):  
Solar Cells ◽  
Etching Process ◽  
Heterojunction Solar Cells ◽  
Koh Etching

Sem Observations in Gastric Mucosa Exposed to Progressive Enzymatic Etching

1980 ◽  
Vol 38 ◽  
pp. 570-571
Author(s):  
C.A.E. Lemmi ◽  
D. Booth ◽  
G.E. Adomian
Keyword(s):  
Gastric Mucosa ◽  
Critical Point ◽  
Proteolytic Activity ◽  
Etching Process ◽  
Cellular Types

In order to enrich populations of homogeneous cellular types we dissociated gastric mucosa by enzymatic techniques. In addition, we used SEM to monitor the progressive etching of the mucosa. Two enzymes were tested: collagenase III with minimum proteolytic activity and Pronase with broader proteolytic effects. The gastric mucosa was exposed to the effect of the enzymes using everted stomach preparations. In this way the digestive action occured progressively from the lumen of the stomach toward the base of the glands. This “etching” process could be monitored conveniently by SEM. After incubation for periods varying from 30 to 210 minutes the tissues were stretched on dental wax, fixed in 2 % glutaralheyde, post-fixed in osmium, dehydrated, critical point dryed and coated with gold. A model MSM-5 “Mini-SEM” was used for observation. Gentle uncurling of the preparation before coating with gold produced fractures which revealed the structure of the gastric glandsin more detail.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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