ArticleNanomaterials (Basel, Switzerland)2024
A Dual-Channel MoS
Article in Nanomaterials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 18 citations in OpenAlex.
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- Preparation and Gas-Sensitive Properties of Square-Star-Shaped Leaf-Like BiVONanomaterials (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Significant progress has been made in two-dimensional material-based sensing devices over the past decade. Organic vapor sensors, particularly those using graphene and transition metal dichalcogenides as key components, have demonstrated excellent sensitivity. These sensors are highly active because all the atoms in the ultra-thin layers are exposed to volatile compounds. However, their selectivity needs improvement. We propose a novel gas-sensing device that addresses this challenge. It consists of two side-by-side sensors fabricated from the same active material, few-layer molybdenum disulfide (MoS₂), for detecting volatile organic compounds like alcohol, acetone, and toluene. To create a dual-channel sensor, we introduce a simple step into the conventional 2D material sensor fabrication process. This step involves treating one-half of the few-layer MoS₂ using ultraviolet-ozone (UV-O
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.