Trends in research on latent heat storage using PCM, a bibliometric analysis
Bibliographic record
Abstract
Latent heat thermal energy storage (LHTES) is a particularly noteworthy thermal energy storage (TES) technology due to its high energy storage density. LHTES involves the use of phase change materials to store thermal energy, which can subsequently be used for heating and cooling applications as well as power generation. This paper outlines the techniques and tools employed to analyse the existing literature on TES and LHTES systems research. Bibliometric, a statistical approach to analysing written publications in specific fields of research, is used to identify significant findings and determine the course of scientific output. A strategic analysis of knowledge development is crucial for detecting opportunities and advancements within the field. This research offers valuable insights into the publication trends within the fields of TES and LHTES over the last three decades. Additionally, the study conducts a thorough examination of the geometric configurations of LHTES systems and their potential impacts on ongoing and future research. By tracking publication rates over 30 years, this study provides a comprehensive overview of how research in TES and LHTES has evolved. Understanding these trends helps researchers and policy makers gauge the growth and relevance of these fields in the context of energy storage and thermal management.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.118 | 0.221 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".