Evidence map›Paper›PMID 42491604›Full record

ArticleFrontiers in chemistry2026

AI-integrated manufacturing of advanced materials for energy storage, catalysis, and environmental applications.

Sai Kumar Punna, Suvarshitha Pusuluru, Madhumita Ravikumar, Farid Menaa

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in chemistry, 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. Article
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.

Sai Kumar Punna *Department of Materials Science and Engineering, University of Houston, Houston, TX, United States.
Suvarshitha Pusuluru *Department of Engineering Data Science, University of Houston, Milwaukee, TX, United States.
Madhumita RavikumarDepartment of Bioengineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical, Chennai, India.
Farid MenaaDepartment of Biomedical and Environmental Engineering (BEE) California Innovations Corporation (CIC), San Diego, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The incorporation of artificial intelligence (AI) into energy systems has become a transformative strategy for tackling global energy related challenges, particularly energy vulnerability (EVI). This work examines how AI contributes to mitigating EVI by evaluating its influence across several dimensions, including energy availability, operational efficiency, consumption patterns, renewable energy integration, and overall energy security. Based on insights derived from machine learning (ML) enabled developments in catalytic materials and CO

Indexed as

artificial intelligencecatalytic materialsCO2 captureelectrocatalysisenergy sustainabilitymachine learningmetal-organic framework

Identifiers

PMID42491604
PMCPMC13377688

What OpenQuestion 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 OpenQuestion graph.