Comprehensive review of emerging trends in thermal energy storage mechanisms, materials and applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Thermal energy storage (TES) technologies are emerging as key enablers of sustainable energy systems by providing flexibility and efficiency in managing thermal resources across diverse applications. This review comprehensively examines the latest advancements in TES mechanisms, materials, and structural designs, including sensible heat, latent heat, and thermochemical storage systems. Recent innovations in nano-enhanced phase change materials (PCMs), hybrid TES configurations, and intelligent system integration are highlighted. The role of advanced computational methods, such as digital twins and AI-based optimization, in enhancing TES performance is also explored. Applications in renewable energy systems, industrial processes, district heating networks, and green hydrogen production are discussed, along with associated challenges and future research directions. This review aims to synthesize current knowledge while identifying pathways for accelerating the development and practical deployment of next-generation TES technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it