Evidence map›Paper›PMID 41768464›Full record

ArticleIndustrial & engineering chemistry research2026

ReDD-COFFEE under the Lens: Revealing Adsorption and Separation Performances of Hypothetical COFs Using Molecular Simulations and Machine Learning.

Hilal Ozyurt, Gokhan Onder Aksu, Hasan Can Gulbalkan, Seda Keskin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Industrial & engineering chemistry research, 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.

  1. Review
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

4 authors.

Hilal OzyurtDepartment of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey.
Gokhan Onder AksuDepartment of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey.ORCID https://orcid.org/0000-0002-0128-4776
Hasan Can GulbalkanDepartment of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey.ORCID https://orcid.org/0000-0001-8001-6018
Seda KeskinDepartment of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey.ORCID https://orcid.org/0000-0001-5968-0336

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this work, we performed a high-throughput computational screening approach combining Grand Canonical Monte Carlo (GCMC) simulations and machine learning (ML) to unlock the potential of the ReDD-COFFEE (Ready-to-use and Diverse Database of Covalent Organic Frameworks with Force field-based Energy Evaluation) database for gas adsorption and separation applications. Molecular simulations were first employed to assess CO

Identifiers

PMID41768464
PMCPMC12947673

What Socratic 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 Socratic graph.