Molecularly Engineered Azobenzene Derivatives for High Energy Density Solid-State Solar Thermal Fuels
Bibliographic record
Abstract
Solar thermal fuels (STFs) harvest and store solar energy in a closed cycle system through conformational change of molecules and can release the energy in the form of heat on demand. With the aim of developing tunable and optimized STFs for solid-state applications, we designed three azobenzene derivatives functionalized with bulky aromatic groups (phenyl, biphenyl, and tert-butyl phenyl groups). In contrast to pristine azobenzene, which crystallizes and makes nonuniform films, the bulky azobenzene derivatives formed uniform amorphous films that can be charged and discharged with light and heat for many cycles. Thermal stability of the films, a critical metric for thermally triggerable STFs, was greatly increased by the bulky functionalization (up to 180 °C), and we were able to achieve record high energy density of 135 J/g for solid-state STFs, over a 30% improvement compared to previous solid-state reports. Furthermore, the chargeability in the solid state was improved, up to 80% charged from 40% charged in previous solid-state reports. Our results point toward molecular engineering as an effective method to increase energy storage in STFs, improve chargeability, and improve the thermal stability of the thin film.
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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) | 0.001 | 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, 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".