ArticleScientific reports2026
Removal of Cr (VI) from wastewater using bentonite as adsorbent: Experimental and Machine Learning investigations.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bentonite material was prepared and examined for the adsorptive removal of Cr (VI) from an aqueous solution. The bentonite was characterized by XRD, FTIR, SEM-EDS, BET and DLS analyses. The experimental design optimization results for the adsorption (AN) of Cr (VI) onto the bentonite adsorbent (AB) determined using the Central Composite Design were pH: 4, adsorbent dose (AD): 1.0 g/100mL, initial Cr (VI) concentration (ICC): 200 mg/L, and contact time (CT): 60 min for a maximum Cr (VI) removal of 86.92%. The Cr (VI) AN onto bentonite fitted to Langmuir isotherm and an AN capacity of 25.17 mg/g was obtained. The kinetics of the process showed Elovich model and the AB also exhibited good reusability over various cycles. Various machine learning methods such as Linear Regression (LR), random forest regressor (RFR), Support Vector Regression (SVR), Gradient Boosting (GB), Artificial Neural Networks (ANNs) were investigated. Among these, the GB and SVR come out as the best model achieved R
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.