Principal Component Analysis-Assisted Detection of Toxic Chemicals Using Atomic Layer Deposition-Grown Zinc Oxide Thin Films

In this study, a highly sensitive and selective gas sensor was developed for detecting hazardous analytes, including ethanol, acetone, hydrogen sulfide (H2S), and hydrogen cyanide (HCN). ZnO thin films were deposited as the sensing layer using atomic layer deposition (ALD), while Au interdigitated electrodes with 5 µm width and spacing were fabricated on SiO2/Si substrates via photolithography. The fabricated sensor exhibited a sensitive response to the target gases even at part-per-billion (ppb). However, it was observed that as the operating temperature decreases, the sensor signal's noise level increases. Additionally, the recovery time for the sensor to return to its baseline value after gas exposure was significantly affected by the operating temperature. The detection limits for ethanol, acetone, H2S, and HCN were 14.6, 35, 115, and 115 ppb, respectively, confirming the sensor's ability to detect all analytes at concentrations well below their threshold limit values. Principal Component Analysis (PCA) revealed well-separated clusters for each analyte, particularly for ethanol and acetone, suggesting that the sensor can effectively discriminate between these two gases. These results demonstrate the sensor’s excellent sensitivity and selectivity supporting its potential for real-time monitoring of toxic gases in environmental and industrial applications.

Authors

Institutions

Publication Details

Journal
Hittite Journal of Science & Engineering
Published
2026-03-31
DOI
https://doi.org/10.17350/hjse19030000371
Primary Topic
Gas Sensing Nanomaterials and Sensors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Principal Component Analysis-Assisted Detection of Toxic Chemicals Using Atomic Layer Deposition-Grown Zinc Oxide Thin Films

Onur Alev
Hittite Journal of Science & Engineering
Gas Sensing Nanomaterials and Sensors
article

Principal Component Analysis-Assisted Detection of Toxic Chemicals Using Atomic Layer Deposition-Grown Zinc Oxide Thin Films

Onur Alev
article en

Abstract

In this study, a highly sensitive and selective gas sensor was developed for detecting hazardous analytes, including ethanol, acetone, hydrogen sulfide (H2S), and hydrogen cyanide (HCN). ZnO thin films were deposited as the sensing layer using atomic layer deposition (ALD), while Au interdigitated electrodes with 5 µm width and spacing were fabricated on SiO2/Si substrates via photolithography. The fabricated sensor exhibited a sensitive response to the target gases even at part-per-billion (ppb). However, it was observed that as the operating temperature decreases, the sensor signal's noise level increases. Additionally, the recovery time for the sensor to return to its baseline value after gas exposure was significantly affected by the operating temperature. The detection limits for ethanol, acetone, H2S, and HCN were 14.6, 35, 115, and 115 ppb, respectively, confirming the sensor's ability to detect all analytes at concentrations well below their threshold limit values. Principal Component Analysis (PCA) revealed well-separated clusters for each analyte, particularly for ethanol and acetone, suggesting that the sensor can effectively discriminate between these two gases. These results demonstrate the sensor’s excellent sensitivity and selectivity supporting its potential for real-time monitoring of toxic gases in environmental and industrial applications.

Hittite Journal of Science & EngineeringVol. 13(1)
Norwegian University of Science and Technology (NO)
Openalex Percentile: Top 29%
Gas Sensing Nanomaterials and Sensors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.