Evidence map›Paper›PMID 31623271›Full record

ArticlePolymers2019

Ultrasound-Assisted Preparation of Chitosan/Nano-Activated Carbon Composite Beads Aminated with (3-Aminopropyl)Triethoxysilane for Adsorption of Acetaminophen from Aqueous Solutions.

Mohammadtaghi Vakili, Parisa Amouzgar, Giovanni Cagnetta, Baozhen Wang, Xiaogang Guo, Amin Mojiri, Ehsan Zeimaran, Babak Salamatinia

Abstract read
In one paragraph

Article in Polymers, 2019. 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. Expertise engineering of ternary BiScientific reports · 2025
    Article
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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

8 authors.

Mohammadtaghi VakiliGreen Intelligence Environmental School, Yangtze Normal University, Chongqing 408100, China. mvakili1981@yahoo.com.
Parisa AmouzgarDiscipline of Chemical Engineering, School of Engineering, Monash University Jalan Lagoon Selatan, Bandar Sunway, Selangor 47500, Malaysia. parisa.amouzgar@gmail.com.
Giovanni CagnettaState Key Joint Laboratory of Environment Simulation and Pollution Control, Beijing Key Laboratory for Emerging Organic Contaminants Control, School of Environment, Tsinghua University, Beijing 100084, China. gcagnetta@mail.tsinghua.edu.cn.
Baozhen WangGreen Intelligence Environmental School, Yangtze Normal University, Chongqing 408100, China. 20170076@yznu.cn.
Xiaogang GuoCollege of Chemistry and Chemical Engineering, Yangtze Normal University, Chongqing 408100, China. guoxiaogang0528@126.com.
Amin MojiriDepartment of Civil and Environmental Engineering, Graduate School of Engineering, Hiroshima University, Higashihiroshima 739-8527, Japan. amin.mojiri@gmail.com.
Ehsan ZeimaranDepartment of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia. ehsanzeimaran@gmail.com.
Babak SalamatiniaDiscipline of Chemical Engineering, School of Engineering, Monash University Jalan Lagoon Selatan, Bandar Sunway, Selangor 47500, Malaysia. babak.salamatinia@monash.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A composite chitosan/nano-activated carbon (CS-NAC) aminated by (3-aminopropyl)triethoxysilane (APTES) was prepared in the form of beads and applied for the removal of acetaminophen from aqueous solutions. NAC and APTES concentrations were optimized to obtain a suitable adsorbent structure for enhanced removal of the pharmaceutical. The aminated adsorbent (CS-NAC-APTES beads) prepared with 40%

Indexed as

acetaminophen adsorptionAPTESchitosancomposite beadsnano-activated carbon

Identifiers

PMID31623271
PMCPMC6835286

What OpenQuestion holds

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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.