Evidence map›Paper›PMID 42131252›Full record

ArticleACS ES&T water2026

A Hybrid Response Surface Methodology and Machine Learning Framework for Quantifying Effects of Physicochemical Parameters on PFAS Distribution.

Harsh V Patel, Jazmin Green, Hyoshin Park, Stephanie Luster-Teasley Pass, Renzun Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACS ES&T water, 2026. 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

5 authors.

Harsh V PatelDepartment of Civil, Architectural, and Environmental Engineering, North Carolina A&T State University, Greensboro, North Carolina 27411, United States.
Jazmin GreenDepartment of Computer Science, North Carolina A&T State University, Greensboro, North Carolina 27411, United States.
Hyoshin ParkDepartment of Engineering Management and Systems Engineering, Old Dominion University, Norfolk, Virginia 23529, United States.
Stephanie Luster-Teasley PassDepartment of Civil, Architectural, and Environmental Engineering, North Carolina A&T State University, Greensboro, North Carolina 27411, United States.
Renzun ZhaoDepartment of Civil, Architectural, and Environmental Engineering, North Carolina A&T State University, Greensboro, North Carolina 27411, United States.ORCID https://orcid.org/0000-0002-2723-2616

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting PFAS adsorption across diverse adsorbents and environmental matrices remains challenging because adsorbent physicochemical properties, PFAS molecular descriptors, and operational conditions simultaneously influence adsorption. This study develops and evaluates a unified hybrid modeling framework that integrates Response Surface Model (RSM) with machine-learning algorithms to quantify how six key variables, surface area, Log

Indexed as

adsorptiondistribution coefficientmachine learningper- and polyfluoroalkyl substance (PFAS)response surface methodology

Identifiers

PMID42131252
PMCPMC13162251

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.