Evidence map›Paper›PMID 38967854›Full record

ArticleJournal of molecular modeling2024

Selective adhesion of nitrogen-containing toxic gasses on hexagonal boron phosphide monolayer: a computational study.

Yuanyuan Zhang, Xiaolei Yan, Ahmed Mahal, Shelesh Krishna Saraswat, Harpreet Kaur, Ahmad J Obaidullah, Yazen M Alawaideh, Talib Kh Hussein, Ahmed Elawady

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of molecular modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Yuanyuan ZhangWeifang Engineering Vocational College, Qingzhou, 262500, China.
Xiaolei YanWeifang Engineering Vocational College, Qingzhou, 262500, China. xiaoleikx1@163.com.
Ahmed MahalDepartment of Medical Biochemical Analysis, College of Health Technology, Cihan University-Erbil, Erbil, Kurdistan Region, Iraq.
Shelesh Krishna SaraswatDepartment of Biotechnology and Genetics, Jain (Deemed-to-be) University, Bengaluru, Karnataka, 560069, India.
Harpreet KaurSchool of Basic & Applied Sciences, Shobhit University, Gangoh, Uttar Pradesh, 247341, India.
Ahmad J ObaidullahDepartment of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P. O. Box 2457, 11451, Riyadh, Saudi Arabia.
Yazen M AlawaidehMEU Research Unit, Middle East University, Amman, Jordan.
Talib Kh HusseinDepartment of Pharmacy, Al-Hadi University College, Baghdad, 10011, Iraq.
Ahmed ElawadyCollege of Technical Engineering, The Islamic University, Najaf, Iraq.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

contextVarious toxic gasses are being released into the environment with the increasing industrialization. However, detecting these gasses at low concentrations has become one of the main challenges in environmental monitoring and protection. Thus, developing sensors with high performance to detect toxic gasses is of utmost significance. For this purpose, researchers have introduced 2D materials thanks to their unique electronic qualities and large specific surface area. Within this piece of research, a hexagonal boron phosphide monolayer (h-BPML) is employed as the substrate material. The adhesion behavior of ambient nitrogen-containing toxic gasses, i.e., N

methodWithin this piece of research, the sensitivity of the h-BPML to four nitrogenous toxic gasses, namely, N

Indexed as

Bader chargesBoron phosphide monolayerGas sensorNitrogen-containing toxic gassesPhysical adhesion

Identifiers

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.