ArticleBiosensors2026
Modeling and Experimental Validation for Detecting Indoor Respiratory Droplet.
Article in Biosensors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Precise quantification of respiratory pathogen transmission is urgently needed to underpin disease surveillance and support diagnostic decision-making in indoor healthcare settings. Although numerical simulations are widely used to study macro-scale transmission, the key physical parameters governing droplet dynamics remain insufficiently understood. This study develops a multi-scale transmission model to evaluate the effects of droplet evaporation, sedimentation, and ventilation on viral transmission. Based on the Wells evaporation-sedimentation theory, a time-varying model is formulated incorporating droplet size distribution, environmental humidity, and ventilation conditions, with analytical expressions derived for concentration distributions across different respiratory activities (breathing, speaking, coughing, and sneezing). Results show that droplet size is decisive for transmission distance and lower relative humidity significantly extends sedimentation range. Small coughing droplets can travel up to 2.5 m, while the bimodal sneezing distribution (1.5 µm and 74 µm) generates high-concentration zones up to 2 m. To validate the model, a molecular communication testbed is developed as a biosensing-oriented platform integrating transmitters, receivers, and configurable ventilation modules with real-time sensing capabilities, enabling systematic verification under varied breathing modes, humidity, and ventilation conditions. Experimental results show strong agreement with theoretical predictions. This framework provides a quantitative basis for biosensing-enabled environmental monitoring and diagnostic-oriented risk assessment, informing ventilation optimization and infection control measures in indoor environments.
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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.