Evidence map›Paper›PMID 32921937›Full record

ArticleRadiation physics and chemistry (Oxford, England : 1993)2020

Calculations of energy deposition and ionization in the 2019 novel coronavirus by electron beam irradiation.

Xiaqi Zhang, Fang Wang, Ming Weng, Meng Cao

Abstract read
In one paragraph

Article in Radiation physics and chemistry (Oxford, England : 1993), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Proposal on the use of Xenon-133 against COVID-19.Radiation physics and chemistry (Oxford, England : 1993) · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Xiaqi ZhangKey Laboratory for Physical Electronics and Devices, Ministry of Education, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China.
Fang WangKey Laboratory for Physical Electronics and Devices, Ministry of Education, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China.
Ming WengKey Laboratory for Physical Electronics and Devices, Ministry of Education, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China.
Meng CaoKey Laboratory for Physical Electronics and Devices, Ministry of Education, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using Monte Carlo methods, this study investigates energy deposition of energetic electrons and ionization in the 2019 novel coronavirus by electron irradiation, which are important characteristic quantities related with biological damage formation. The inelastic scattering of low-energy electrons (<10 keV) was calculated by dielectric theory. The optical energy-loss functions of viral proteins and RNA were derived from an empirical method in the energy-loss range <40 eV and the calculation of optical parameters of the biomolecules. The densities and distributions of energy deposition and ionization were calculated from the stopping power and inelastic cross-sections in the electron-cascade simulation. Electrons with primary energies of approximately 1-3 keV produced significant energy deposition and ionization in the target coronavirus. More energetic electrons were less effective due to the larger electron range and fewer scattering events in the coronavirus.

Indexed as

Electron irradiationEnergy depositionIonization

Identifiers

PMID32921937
PMCPMC7473055

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.