Heat to H<sub>2</sub>
Bibliographic record
Abstract
Renewable energy sources are often intermittent or unavailable for the end user, making energy storage vital. One way to store the energy is in hydrogen, and our aim is to produce hydrogen through reverse electrodialysis (RED) [1, 2, 3]. RED is a technology that uses concentration differences to create electrical energy. This is achieved through the use of ion-conducting membranes, where the membranes separate two solutions while producing an electric potential. Moreover, RED can be used to produce hydrogen in addition to electric energy. An illustration of the RED-cell is given in Figure 1. Any salt can be used for this purpose, as the potential is dependent on concentration differences only, not the specific solution used. This project utilises waste heat of low quality to build up and maintain the concentration differences (see Figure 2). The whole process is carried out in a closed system, where heat will be the only external input (in addition to electricity used for pumps). The closed energy storage system can easily be installed in connection with industrial processes where waste heat is available. As ions are transported from the concentrated to the diluted solution, the potential will decrease. Two separation techniques are evaluated for increasing the concetration difference, namely evaporation at high temperature or precipitation at low temperature. The respective process sketches are shown in Figure 2. This research investigates the suggested system with either NaCl or KNO 3 as active solutions and temperature levels. Parts of the experiments include an end goal of creating sufficient potentials for hydrogen evolution, hence creating a technology where low-grade waste heat is converted to hydrogen. [1] O. S. Burheim, J. G. Pharoah, D. Vermaas, B. B. Sales, K. Nijmeijer, and H. V. Hamelers, "Reverse electrodialysis," Encyclopedia of Membrane Science and Technology , 2013. [2] M. F. M. Bijmans, O. S. Burheim, M. Bryjak, A. Delgado, P. Hack, F. Mantegazza, S. Tenisson, and H. V. M. Hamelers, "CAPMIX - Deploying capacitors for salt gradient power extraction," Energy Procedia , vol. 20, pp. 108-115, 2012. [3] O. S. Burheim, Engineering Energy Storage . Academic Press 2018, 2017. Figure 1
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.002 |
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 teacher head, 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".