|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM354526790 |
| 003 |
DE-627 |
| 005 |
20231226062420.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2023 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1021/acs.langmuir.2c03465
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1181.xml
|
| 035 |
|
|
|a (DE-627)NLM354526790
|
| 035 |
|
|
|a (NLM)36947443
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Yoon, Sung-Ho
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Extreme Gradient Boosting to Predict Atomic Layer Deposition for Platinum Nano-Film Coating
|
| 264 |
|
1 |
|c 2023
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 11.04.2023
|
| 500 |
|
|
|a Date Revised 15.04.2023
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Extreme gradient boosting (XGBoost) is an artificial intelligence algorithm capable of high accuracy and low inference time. The current study applies this XGBoost to the production of platinum nano-film coating through atomic layer deposition (ALD). In order to generate a database for model development, platinum is coated on α-Al2O3 using a rotary-type ALD equipment. The process is controlled by four parameters: process temperature, stop valve time, precursor pulse time, and reactant pulse time. A total of 625 samples according to different process conditions are obtained. The ALD coating index is used as the Al/Pt component ratio through ICP-AES analysis during postprocessing. The four process parameters serve as the input data and produces the Al/Pt component ratio as the output data. The postprocessed data set is randomly divided into 500 training samples and 125 test samples. XGBoost demonstrates 99.9% accuracy and a coefficient of determination of 0.99. The inference time is lower than that of random forest regression, in addition to a higher prediction safety than that of the light gradient boosting machine
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Jeon, Jun-Hyeok
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Cho, Seung-Beom
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Nacpil, Edric John Cruz
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Jeon, Il
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Choi, Jae-Boong
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Kim, Hyeongkeun
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t Langmuir : the ACS journal of surfaces and colloids
|d 1992
|g 39(2023), 14 vom: 11. Apr., Seite 4984-4992
|w (DE-627)NLM098181009
|x 1520-5827
|7 nnns
|
| 773 |
1 |
8 |
|g volume:39
|g year:2023
|g number:14
|g day:11
|g month:04
|g pages:4984-4992
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1021/acs.langmuir.2c03465
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_22
|
| 912 |
|
|
|a GBV_ILN_350
|
| 912 |
|
|
|a GBV_ILN_721
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 39
|j 2023
|e 14
|b 11
|c 04
|h 4984-4992
|