AIMC Topic: Aerosols

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An Aerosol Jet-Printed Wearable Graphene/Cellulose Nanocrystal Acoustic Sensor for Speech Recognition.

ACS sensors
Wearable acoustic sensors offer a promising solution for effective communication for individuals with speech impairments by calibrating throat vibrations and converting them to synthesized speech. We developed a new type of piezoresistive acoustic se...

Divergent radiative forcing of fine-mode aerosols across tree genera during wildfires in North America and Europe.

Journal of hazardous materials
Rising global temperatures have escalated wildfire frequency, and the resulting fine-mode aerosol emissions pose a significant threat to the global ecosystem. However, how these emissions vary with tree genera remains largely unexplored. In this stud...

Oxygenated Organic Molecules over the Boundary Layer Aloft in Beijing.

Environmental science & technology
Oxygenated organic molecules (OOMs) originate from both direct emissions and secondary formation via the oxidation of volatile organic compounds (VOCs) emitted from biogenic and anthropogenic sources. OOMs are suggested to play a crucial role in the ...

Decoding PM oxidative potential in Ningbo, China: Key chemicals, sources, and health risks via dual-assay and machine learning.

Journal of hazardous materials
PM oxidative potential (OP), a key driver of health risks, was investigated in Ningbo, China, using dual dithiothreitol (DTT) and ascorbic acid (AA) assays combined with machine learning (ML). This approach accounts for the complexity of interactions...

Recent advances in dry powder inhalation formulations prepared by co-spray drying technology: a comprehensive review.

International journal of pharmaceutics
Co-spray drying technology represents an increasingly important approach in preparing dry powder inhalation (DPI) formulations. Compared to conventional spray drying, co-spray drying typically yields particles characterized by improved aerosol perfor...

Exploring Aerosol Vertical Distributions and Their Influencing Factors: Insight from MAX-DOAS and Machine Learning.

Environmental science & technology
Understanding aerosol vertical distribution is crucial for aerosol pollution mitigation but is hindered by limited observational data. This study employed multiaxis differential optical absorption spectroscopy (MAX-DOAS) technology with a coupled rad...

Particulate matter estimation using satellite datasets: a machine learning approach.

Environmental science and pollution research international
In the present work, it is the first time an interpretable machine learning model has been developed for the estimation of Particulate Matter 10 µm (PM) concentrations over India using Aerosol Optical Depth (AOD) from two different satellites, i.e. I...

Predictions of the Optical Properties of Brown Carbon Aerosol by Machine Learning with Typical Chromophores.

Environmental science & technology
The linkages between BrC optical properties and chemical composition remain inadequately understood, with quantified chromophores explaining less than 25% of ambient aerosol light absorption. This study characterized 38 typical chromophores in aeroso...

Quantification of Size-Binned Particulate Matter in Electronic Cigarette Aerosols Using Multi-Spectral Optical Sensing and Machine Learning.

Sensors (Basel, Switzerland)
To monitor health risks associated with vaping, we introduce a multi-spectral optical sensor powered by machine learning for real-time characterization of electronic cigarette aerosols. The sensor can accurately measure the mass of particulate matter...

Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval.

Environmental science & technology
Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for clim...