Enhancing thermal conductivity of novel ternary nitrate salt mixtures for thermal energy storage (TES) fluid
Bibliographic record
Abstract
Efficient thermal energy storage (TES) is crucial for concentrated solar power (CSP) plants, necessitating the exploration of advanced heat transfer fluids with enhanced thermal conductivity. Conventional binary nitrate salt mixtures have limitations in thermal performance, prompting research into ternary mixtures and nanoparticle additives. This study investigates novel ternary nitrate salt mixtures comprising potassium nitrate (KNO₃), lithium nitrate (LiNO₃), and magnesium nitrate hexahydrate (Mg(NO₃)₂·6 H₂O) as high-performance TES fluids during the during the heat absorption phase. Seven different salt compositions were synthesized and characterized using the laser flash technique to evaluate their thermal conductivity over 100–400°C. Results revealed a significant influence of composition on thermal conductivity, with maximum values during melting ranging from 0.0777 W/m·K to 0.7373 W/m·K. Melting points varied from 335 K to 340.39 K, demonstrating tailorability through compositional adjustments. Furthermore, incorporation of 0.5 wt% aluminum oxide (Al₂O₃) nanoparticles resulted in substantial thermal conductivity enhancements, with the most significant increase observed in TSF10 (from 0.0777 W/m·K to 0.491 W/m·K). These improvements are attributed to enhanced phonon transport, increased surface area, and Brownian motion facilitated by Al₂O₃ nanoparticles. The study provides a comprehensive cost analysis, including raw material costs, and discusses the potential efficiency gains for CSP applications. The findings contribute to the development of high-performance and cost-effective TES fluids, advancing the efficiency and viability of sustainable energy generation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".