Evidence map›Paper›PMID 36252897›Full record

ArticleChemosphere2023

SARS-CoV-2 removal by mix matrix membrane: A novel application of artificial neural network based simulation in MATLAB for evaluating wastewater reuse risks.

Sasan Zahmatkesh, Yousof Rezakhani, Abdoulmohammad Gholamzadeh Chofreh, Melika Karimian, Chongqing Wang, Iman Ghodrati, Mudassir Hasan, Mika Sillanpaa, Hitesh Panchal, Ramsha Khan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Chemosphere, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

10 authors.

Sasan ZahmatkeshDepartment of Chemical Engineering, University of Science and Technology of Mazandaran, P.O. Box 48518-78195, Behshahr, Iran; Tecnologico de Monterrey, Escuela de Ingenieríay Ciencias, Puebla, Mexico. Electronic address: sasan-zahmatkesh@mazust.ac.ir.
Yousof RezakhaniDepartment of Civil Engineering, Pardis Branch, Islamic Azad University, Pardis, Iran.
Abdoulmohammad Gholamzadeh ChofrehSustainable Process Integration Laboratory, SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology, VUT Brno, Technická 2896/2, 616 00, Brno, Czech Republic.
Melika KarimianFaculty of Civil Engineering, Architecture and Urban Planning, University of Eyvanekey, Iran.
Chongqing WangSchool of Chemical Engineering, Zhengzhou University, Zhengzhou, 450001, China.
Iman GhodratiDepartment of Computer Engineering, Bojnourd Branch, Islamic Azad University, Bojnourd, Iran.
Mudassir HasanDepartment of Chemical Engineering, College of Engineering, King Khalid University, Abha, 61411, Saudi Arabia.
Mika SillanpaaFaculty of Science and Technology, School of Applied Physics, University Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia; International Research Centre of Nanotechnology for Himalayan Sustainability (IRCNHS), Shoolini University, Solan, 173212, Himachal Pradesh, India; Department of Chemical Engineering, School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg, P. O. Box 17011, Doornfontein, 2028, South Africa.
Hitesh PanchalMechanical Engineering Department, Government Engineering College, Patan, Gujarat, India.
Ramsha KhanFaculty of Civil Engineering, Institute of Technology, Shri Ramswaroop Memorial University, Barabanki, 225003, UP, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 outbreak led to the discovery of SARS-CoV-2 in sewage; thus, wastewater treatment plants (WWTPs) could have the virus in their effluent. However, whether SARS-CoV-2 is eradicated by sewage treatment is virtually unknown. Specifically, the objectives of this study include (i) determining whether a mixed matrixed membrane (MMM) is able to remove SARS-CoV-2 (polycarbonate (PC)-hydrous manganese oxide (HMO) and PC-silver nanoparticles (Ag-NP)), (ii) comparing filtration performance among different secondary treatment processes, and (iii) evaluating whether artificial neural networks (ANNs) can be employed as performance indicators to reduce SARS-CoV-2 in the treatment of sewage. At Shariati Hospital in Mashhad, Iran, secondary treatment effluent during the outbreak of COVID-19 was collected from a WWTP. There were two PC-Ag-NP and PC-HMO processes at the WWTP targeted. RT-qPCR was employed to detect the presence of SARS-CoV-2 in sewage fractions. For the purposes of determining SARS-CoV-2 prevalence rates in the treated effluent, 10 L of effluent specimens were collected in middle-risk and low-risk treatment MMMs. For PC-HMO, the log reduction value (LRV) for SARS-CoV-2 was 1.3-1 log10 for moderate risk and 0.96-1 log10 for low risk, whereas for PC-Ag-NP, the LRV was 0.99-1.3 log10 for moderate risk and 0.94-0.98 log10 for low risk. MMMs demonstrated the most robust absorption performance during the sampling period, with the least significant LRV recorded in PC-Ag-NP and PC-HMO at 0.94 log10 and 0.96 log10, respectively.

Indexed as

COVID-19Metal NanoparticlesHumansNeural Networks, ComputerSARS-CoV-2SewageSilverWastewaterSewageSilverWastewaterArtificial neural networkMix matrix membraneSARS-CoV-2Wastewater treatment

Identifiers

PMID36252897
PMCPMC9560862

What Socratic 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 Socratic graph.