Evidence map›Paper›PMID 38082488›Full record

ReviewACS applied materials & interfaces2025

Evaluating CH

Hasan Can Gulbalkan, Alper Uzun, Seda Keskin

Open access · hybridAbstract readReview
In one paragraph

Review in ACS applied materials & interfaces, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.1field-weighted citation impact, top 25% of its field
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

2 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. 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

3 authors at 1 institution in 1 country.

Hasan Can GulbalkanDepartment of Chemical and Biological Engineering, Koç University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey.
Alper UzunDepartment of Chemical and Biological Engineering, Koç University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey.ORCID 0000-0001-7024-2900
Seda KeskinDepartment of Chemical and Biological Engineering, Koç University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey.ORCID 0000-0001-5968-0336
Koç University · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Considering the large abundance and diversity of metal-organic frameworks (MOFs), evaluating the gas adsorption and separation performance of the entire MOF material space using solely experimental techniques or brute-force computer simulations is impractical. In this study, we integrated high-throughput molecular simulations with machine learning (ML) to explore the potential of both synthesized, the real MOFs, and computer-generated, the hypothetical MOFs (hypoMOFs), for adsorption-based CH

Indexed as

adsorptiongas separationmachine learningmetal−organic frameworkmolecular simulation

Identifiers

PMID38082488
PMCPMC11956003
OpenAlexW4389617354

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.