Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article aims to develop an entropy based method of systematically improving efficiency of fuel cells. Entropy production of both electrochemical and thermofluid irreversibilities is formulated based on the Second Law. Ohmic, concentration, and activation irreversibilities occur within the electrodes, while thermal and friction irreversibilities occur within the fuel channel. These irreversibilities reduce the overall cell efficiency by generating voltage losses. Unlike past studies, this article considers fuel channel irreversibilities within the total entropy production, for both solid oxide fuel cells (SOFCs) and proton exchange membrane fuel cells (PEMFCs). Predicted results of entropy production are shown at varying operating temperatures, surface resistances, and channel configurations. Numerical predictions are compared successfully against past measured data of voltage profiles, thereby providing useful validation of the entropy based formulation. The Second Law stipulates the maximum theoretical capability of energy conversion within the fuel cell. Unlike past methods characterizing voltage losses through overpotential or polarization curves, the entropy based method provides a useful alternative and systematic procedure for reducing voltage losses.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it