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Volumn 114, Issue 33, 2010, Pages 14042-14049

Monolayer-capped cubic platinum nanoparticles for sensing nonpolar analytes in highly humid atmospheres

Author keywords

[No Author keywords available]

Indexed keywords

11-MERCAPTOUNDECANOIC ACID; AIR MIXTURES; ANALYTES; CHEMIRESISTORS; FAST RESPONSE; HIGH HUMIDITY; HIGH SENSITIVITY; HUMID ATMOSPHERES; LOW COSTS; LOW SENSITIVITY; NON-POLAR; NON-POLAR LIGANDS; NONPOLAR MOLECULES; OLEYLAMINE; ORGANIC LIGANDS; PLATINUM NANOPARTICLES; PT NANOPARTICLES; SENSING APPLICATIONS; SENSING MECHANISM;

EID: 77955897896     PISSN: 19327447     EISSN: 19327455     Source Type: Journal    
DOI: 10.1021/jp105810w     Document Type: Article
Times cited : (74)

References (58)
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    • (2007) J. Phys. D , vol.40 , pp. 7173-7186
    • Haick, H.1
  • 44
    • 77955909640 scopus 로고    scopus 로고
    • Different devices with the same coating showed similar results, within ±5% deviations, mostly much higher than the device-to-device variations. Therefore, the observed discrimination effects can be attributed to the influence of the organic coating
    • Different devices with the same coating showed similar results, within ±5% deviations, mostly much higher than the device-to-device variations. Therefore, the observed discrimination effects can be attributed to the influence of the organic coating.
  • 45
    • 77955883832 scopus 로고    scopus 로고
    • Different devices with the same coating showed similar results, within ±5% deviations, mostly much higher than the device-to-device variations. Therefore, the observed discrimination effects can be attributed to the influence of the organic coating
    • Different devices with the same coating showed similar results, within ±5% deviations, mostly much higher than the device-to-device variations. Therefore, the observed discrimination effects can be attributed to the influence of the organic coating.
  • 47
    • 77955900822 scopus 로고    scopus 로고
    • PCA finds projection weights for sensor response data that maximize total response variance in principal components, where the dimension capturing most sensor variance is given by PC1, and the dimension capturing the second most variance (subject to being orthogonal to PC1) is given by PC2, etc
    • PCA finds projection weights for sensor response data that maximize total response variance in principal components, where the dimension capturing most sensor variance is given by PC1, and the dimension capturing the second most variance (subject to being orthogonal to PC1) is given by PC2, etc.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.