ArticleScientific reports2024
Deep learning artificial neural network framework to optimize the adsorption capacity of 3-nitrophenol using carbonaceous material obtained from biomass waste.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Sequestration of methyl blue dye from aqueous solution onto raw and modified Banana (Musa paradisiaca) flower wastes.Scientific reports · 2026Article
- Modeling and optimization of methylene blue adsorption on biochar via artificial neural network (ANN).Biodegradation · 2026Article
- Predictive modeling of MB adsorption on activated olive stone through artificial neural networks.Scientific reports · 2025Article
- Artificial Neural Networks for Impact Strength Prediction of Composite Barriers.Materials (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The presence of toxic chemicals in water, including heavy metals like mercury and lead, organic pollutants such as pesticides, and industrial chemicals from runoff and discharges, poses critical public health and environmental risks leading to severe health issues and ecosystem damage; education plays a crucial role in mitigating these effects by enhancing awareness, promoting sustainable practices, and integrating environmental science into curricula to empower individuals to address and advocate for effective solutions to water pollution. However, the educational transformation should be accompanied with a technical process which can be eventually transferred to society to empower environmental education. In this study, carbonaceous material derived from Haematoxylum campechianum (CM-HC) was utilized for removing 3-nitrophenol (3-Nph) from aqueous solutions. The novelty of this research utilizes Haematoxylum campechianum bark and coconut shell, abundant agricultural wastes in Campeche, Mexico, for toxin removal, enhancing the adsorption process through artificial neural networks and genetic algorithms to optimize conditions and maximize the absorption efficiency. CM-HC's surface morphology was analyzed using scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), and pHpzc. Kinetic models including pseudo-first-order (PFO), pseudo-second-order (PSO), and Elovich were applied to fit the data. Adsorption isotherms were determined at varying pH (3-8), adsorbent dosages (2-10 g/L), and temperatures (300.15-330.15 K), employing Langmuir, Freundlich, Temkin, and Redlich-Peterson models. PSO kinetics demonstrated a good fit (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.