| 1. |
- Darmadi, Iwan, 1990, et al.
(författare)
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Optimization of the Composition of PdAuCu Ternary Alloy Nanoparticles for Plasmonic Hydrogen Sensing
- 2021
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Ingår i: ACS Applied Nano Materials. - : American Chemical Society (ACS). - 2574-0970. ; 4:9, s. 8716-8722
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Tidskriftsartikel (refereegranskat)abstract
- Alloying is a long-standing central strategy in materials science for the tailoring and optimization of bulk material properties, which more recently has started to find application also in engineered nanomaterials and nanostructures used in, among other, nanoplasmonic hydrogen sensors. Specifically, alloying Pd nanoparticles to form binaries and ternaries with the coinage metals Au and Cu has proven efficient to mitigate hysteresis in the sensor response, improve response and recovery times, boost sensitivity in the low hydrogen concentration sensing range, and reduce the detrimental impact of carbon monoxide poisoning. However, when surveying the corresponding studies, it is clear that there is a trade-off between the sensitivity enhancement and the CO-poisoning resistance effects provided by Au and Cu alloyants, respectively. Therefore, in this work, we systematically screen the impact of the Au and Cu concentration in PdAuCu ternary alloy nanoparticles used for plasmonic hydrogen sensing, to obtain a champion system with maximized sensitivity and CO-poisoning resistance based on an evaluation using the stringent ISO 26142 test protocol. As the main results, we find that the best hysteresis-free and sensitive response combined with deactivation resistance to 500 ppm CO in synthetic air is obtained for the Pd65Au25Cu10 ternary alloy system, which also exhibits good long-term stability during operation under severe CO poisoning conditions.
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| 2. |
- Tomecek, David, 1991, et al.
(författare)
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Neural network enabled nanoplasmonic hydrogen sensors with 100 ppm limit of detection in humid air
- 2024
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Ingår i: Nature Communications. - 2041-1723 .- 2041-1723. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- Environmental humidity variations are ubiquitous and high humidity characterizes fuel cell and electrolyzer operation conditions. Since hydrogen-air mixtures are highly flammable, humidity tolerant H2 sensors are important from safety and process monitoring perspectives. Here, we report an optical nanoplasmonic hydrogen sensor operated at elevated temperature that combined with Deep Dense Neural Network or Transformer data treatment involving the entire spectral response of the sensor enables a 100 ppm H2 limit of detection in synthetic air at 80% relative humidity. This significantly exceeds the <1000 ppm US Department of Energy performance target. Furthermore, the sensors pass the ISO 26142:2010 stability requirement in 80% relative humidity in air down to 0.06% H2 and show no signs of performance loss after 140 h continuous operation. Our results thus demonstrate the potential of plasmonic hydrogen sensors for use in high humidity and how neural-network-based data treatment can significantly boost their performance.
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