ReviewRSC advances2026
Critical review of adsorption technologies for heavy metal removal: mechanistic, computational, and policy perspectives.
Review in RSC advances, 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The main sources of heavy metal pollution in groundwater include discharges from petroleum refining plants, agricultural runoff, hospital wastewater, and environmental damage caused by war. Advances in adsorbents provide safe, efficient, and economical techniques for contaminant removal. This study presents a critical review of adsorption applications worldwide, particularly in Iraq, evaluating the effectiveness, large-scale applicability, and long-term sustainability of selected adsorbents. Furthermore, the study focuses on adsorption mechanisms, criteria for selecting appropriate adsorbents, process design, and advanced computational methods such as machine learning algorithms and molecular dynamics simulations. Biochar prepared from rice husk removes over 90% of Pb
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.