High-temperature high-pressure calorimetry for studying thermochemical energy storage materials
Bibliographic record
Abstract
Metal hydrides that can hydrogenate and dehydrogenate reversibly at high-temperature and high-pressure are considered candidate materials for thermochemical energy storage (TES). The thermodynamic and kinetic properties of these metal hydrides are poorly defined due to the limitation of the measurement techniques at high-temperature and high pressure. This thesis describes a custom-designed high-temperature and high-pressure calorimeter for studying gram-scale heterogeneous chemical reactions that expands the limits of TES research. Chapter 2 outlines the physical design and fabrication details of the calorimeter. The calorimeter was demonstrated operating up to at least 1232 °C while under pure hydrogen pressures up to 33 bar with simultaneous in-situ calorimetric and pressure measurements. A finite element analysis (FEA) model was constructed using COMSOL Multiphysics to aid the experimental design. Chapter 3 outlines a thermal analysis algorithm that is modelled with a lumped-element model. The chemical enthalpy can be estimated using this algorithm along with the power and temperature profile data from this calorimeter. The model was verified using an aluminum heat of fusion experiment, as well as an exothermic process simulation to obtain a fitting accuracy of over 99%. Chapter 4 summarizes this work and offers future directions of the research that can be conducted using this instrument.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".