Industry 4.0 enabled calorimetry and heat transfer for renewable energy systems.

Emmanuel O Atofarati, Christopher C Enweremadu

Abstract readReview

In one paragraph

Review in iScience, 2025. 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.

AI-Carbon-Energy: Spillover effects and drivers in interconnected markets.iScience · 2026
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.

Emmanuel O AtofaratiDepartment of Mechanical, Bioresources and Biomedical Engineering, University of South Africa, Science Campus, Florida, Johannesburg 1710, South Africa.

Christopher C EnweremaduDepartment of Mechanical, Bioresources and Biomedical Engineering, University of South Africa, Science Campus, Florida, Johannesburg 1710, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of the fourth industrial revolution technologies, including artificial intelligence (AI), machine learning (ML), the Internet of Things (IOT), digital twins, and blockchain, is advancing calorimetry and heat transfer in renewable energy systems. This review examines how these technologies improve thermal efficiency, enable real-time system monitoring, and support predictive maintenance across solar, wind, geothermal, and bioenergy applications. AI-driven models are discussed for optimizing complex heat transfer behaviors, while IoT frameworks facilitate continuous calorimetric data acquisition. Digital twins support virtual simulations, and blockchain ensures data security. A comprehensive evaluation of recent research identifies key challenges such as computational demands, data security, and policy gaps. The article proposes future directions such as developing hybrid AI-physics models, enhancing explainable AI, conducting long-term performance validation, and standardization frameworks to enable the reliable deployment of smart thermal management systems for renewable energy.

Indexed as

Energy engineeringEnergy sustainabilityHeat transfer

Identifiers

PMID40697411

DOI10.1016/j.isci.2025.112994

PMCPMC12281055

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.