In Silico Models for Predicting Adsorption of Organic Pollutants on Atmospheric Nanoplastics by Combining Grand Canonical Monte Carlo/Density Functional Theory and Quantitative Structure Activity Relationship Approach.

Ya Wang, Honghong Yi, Chao Li, Xiaolong Tang, Peng Zhao, Zhongfang Chen

Abstract read

In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Integrating DFT Computations and QSAR Modeling to Predict Adsorption of Organic Pollutants onto Microplastics in Aqueous Environments.Materials (Basel, Switzerland) · 2026
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

6 authors.

Ya WangSchool of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.ORCID 0000-0003-3101-0597

Honghong YiSchool of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Chao LiEngineering Laboratory for Water Pollution Control and Resources Recovery, School of Environment, Northeast Normal University, Changchun 130117, China.

Xiaolong TangSchool of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Peng ZhaoSchool of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Zhongfang ChenDepartment of Chemistry, University of Puerto Rico, San Juan, PR 00931, USA.

Funding

the Fundamental Research Funds for the Central Universities FRF-BD-25-003the Fundamental Research Funds for the Central Universities FRF-TP-22-087A1the National Key R&D Program of China 2023YFB3810800the National Natural Science Foundation of China 22206095

6 · The paper itself

Abstract

Estimating the adsorption data and understanding the adsorption behavior and mechanism of organic pollutants on nanoplastics are crucial for assessing their ecological risks. Herein, in silico techniques, i.e., grand canonical Monte Carlo simulations, density functional theory computations, and quantitative structure activity relationship (QSAR) modeling, were integrated to examine the adsorption of 39 representative aliphatic and aromatic compounds and nine emerging pollutants (brominated flame retardants and phosphorus flame retardants) onto 12 different nanoplastics under atmospheric conditions. Three QSAR models were constructed to predict the adsorption equilibrium constant (log

Indexed as

adsorption capacityadsorption equilibrium constantdensity functional theory (DFT) calculationsgrand canonical Monte Carlo (GCMC) simulationnanoplasticspredictive model

Identifiers

PMID41677528

DOI10.3390/nano16030178

PMCPMC12899699

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.