ReviewAir quality, atmosphere, & health2021
A review on recent trends of antiviral nanoparticles and airborne filters: special insight on COVID-19 virus.
Review in Air quality, atmosphere, & health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Synthesis and Antiviral Activity of Nanowire Polymers Activated with Ag, Zn, and Cu Nanoclusters.Pharmaceutics · 2025Article
- Advanced Electrospinning Technology Applied to Polymer-Based Sensors in Energy and Environmental Applications.Polymers · 2024Review
- In Vitro Antiviral Activity of Hyperbranched Poly-L-Lysine Modified by L-Arginine against Different SARS-CoV-2 Variants.Nanomaterials (Basel, Switzerland) · 2023Article
- Preliminary Evaluation ofACS omega · 2023Article
- Polyvinylidene fluoride multi-scale nanofibrous membrane modified using N-halamine with high filtration efficiency and durable antibacterial properties for air filtration.Journal of colloid and interface science · 2022Article
- Preparation and Characterization of Electrospun PAN-CuClPolymers · 2022Article
- Recent breakthroughs in nanostructured antiviral coating and filtration materials: a brief review.RSC advances · 2022Review
- Fabrication of air filters with advanced filtration performance for removal of viral aerosols and control the spread of COVID-19.Advances in colloid and interface science · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Novel corona virus (COVID-19) pandemic in the last 4 months stimulates the international scientific community to search for vaccine of antiviral agents suitable for in activating the virus inside and outside the human body. More than 4 million people globally are infected by the virus and about 300,000 dead cases until this moment. The ventilation and airborne filters are also investigated aiming to develop an efficient antiviral filtration technology. Human secretion of the infected person as nasal or saliva droplets goes as airborne and distributes the virus everywhere around the person. N95 and N98 filters are the must use filters for capturing particles of sizes around 300 nm. The average size of the novel corona virus (COVID-19) is 100 nm and there is no standard or special filter suitable for this virus. The nanoparticle-coated airborne filter is a suitable technique in this regard. While the efficiency of this type of filters still needs to be enhanced, new developed nanofiber filters are proposed. Most recently, the charged nanofiber filters of sizes below 100 nm are developed and provide an efficient viral filtration and inactivation. The efficiency of filter must be kept at accepted level without increasing the pressure drop. The present review outlines the most efficient antiviral nanoparticles including the recent functional nanoparticles. The filtration theory, filtration modeling, filter testing, and different types of filter with special concentration on the charged nanofiber filter were discussed. The charged nanofiber filter able to capture novel corona virus (COVID-19) with 94% efficiency and a pressure drop less than 20 MPa.
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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.