Evidence map›Paper›PMID 39628588›Full record

ReviewiScience2024

Radiative cooling technology with artificial intelligence.

Yeongju Jung, Seung Hwan Ko

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in iScience, 2024. 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

2 authors.

Yeongju JungApplied Nano and Thermal Science Lab, Department of Mechanical Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea.
Seung Hwan KoApplied Nano and Thermal Science Lab, Department of Mechanical Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As sustainable thermal management becomes a global priority, the development of radiative cooling (RC) technology has recently emerged as a promising solution. Simultaneously, recent advent of artificial intelligence (AI) offers the potential to revolutionize current research in sustainable cooling strategies. This article discusses the advancement of radiative cooling technology through the integration of AI, tackling the challenging issues arising from the conventional approach and offering strategic solutions to address global issues. AI, capable of mimicking or exceeding human capabilities through various algorithms, enables the efficient optimization of RC structures. Moreover, integrating AI with advanced RC technologies, which have the potential to surpass traditional RC configurations and applications but are still in the early stages, can further accelerate progress in the field of RC. Hence, AI-driven RC technologies will contribute to addressing the increasingly prevalent environmental challenges, further being a leading solution for next-generation sustainable thermal managements as these technologies continue to mature.

Indexed as

Artificial intelligenceEnergy managementEngineering

Identifiers

PMID39628588
PMCPMC11612785

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.