Mapping the research landscape: energy storage of bio-nanoparticle-enhanced phase change materials for solar desalination—a scientometric framework
Bibliographic record
Abstract
Solar desalination is an economical and eco-friendly approach to producing potable water, particularly in remote areas. Nevertheless, the limited efficiency of traditional solar panels restricts their ability to fulfill the growing demand for clean water. This study focuses on improving the performance of single-basin solar stills (SBS) by incorporating paraffin wax as a phase change material (PCM) with copper nanoparticles (Cu NPs) and agro-based materials in a stepped design. A scientometric analysis framework was applied to map the research landscape, followed by an experimental evaluation in which SBS units were fabricated and tested under controlled solar exposure with varying combinations of PCM, Cu NPs, and concentrators. The research is tailored to accommodate diverse climatic and operational conditions. Results reveal that the combination of PCM and Cu NPs significantly enhances freshwater output compared to traditional setups. The modified SBS demonstrated a productivity improvement of 67.18% for single-effect and 125% for double-effect configurations. The use of PCM alone resulted in a 21.5% boost in productivity, while the SBS design excluding CuO-based nanofluids achieved approximately 32% higher freshwater generation by utilizing solar energy. Moreover, combining a concentrator with PCM led to an additional 26% increase in efficiency. These results highlight the potential of integrating PCM and nanoparticles as an effective strategy to optimize SBS performance for sustainable water desalination.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".