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	<title>hydrogen sensing &#8211; BIOENGINEER.ORG</title>
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		<title>Machine Learning Framework Brings Order to the Chaotic World of Gas Sensor Data</title>
		<link>https://bioengineer.org/machine-learning-framework-brings-order-to-the-chaotic-world-of-gas-sensor-data/</link>
		
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		<pubDate>Sun, 11 Oct 2026 03:23:52 +0000</pubDate>
				<category><![CDATA[Chemistry]]></category>
		<category><![CDATA[ammonia]]></category>
		<category><![CDATA[carbon monoxide]]></category>
		<category><![CDATA[DBSCAN clustering]]></category>
		<category><![CDATA[gas sensors]]></category>
		<category><![CDATA[hydrogen sensing]]></category>
		<category><![CDATA[Hydrogen sulfide]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[MEMS]]></category>
		<category><![CDATA[metal oxides]]></category>
		<category><![CDATA[nitrogen dioxide]]></category>
		<category><![CDATA[principal component analysis]]></category>
		<category><![CDATA[sensitivity standardization]]></category>
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					<description><![CDATA[Researchers have built a data-driven framework using principal component analysis and DBSCAN clustering to standardize and compare 91 gas sensors across five target gases, revealing that material class, operating temperature, and transduction modality govern sensor performance.]]></description>
		
		
		
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