Optimization of Anion Exchange Membranes Derived from Cross-linked Chitosan-Poly (diallyldimethylammonium chloride) for All-solid Electrochemical Capacitors
Bibliographic record
Abstract
Carbon dioxide can be formed in many ways, especially in combustion, it is one of the most important reasons lead to greenhouse effect and global warming. So many scientists devoted themselves to techniques that can reduce CO2 emissions. Among these promising techniques, electrochemical ways provide a efficient means, as it can convert carbon dioxide into fuels and other useful materials.[1,2] This way has many advantages: (a) the process is controllable by setting the temperature of electrode; (b) the system can take advantage of new energy resources and the production do not contaminate environment; (c) the system is compact and easily operated; (d) the experiment can be operated under normal atmospheric pressure and temperature.[3,4] In this paper, Tin-Cu alloy was deposited on carbon paper by electrodeposition in aqueous solution, which was employed as a cathode for electrochemical reduction of CO2 to formate, and how to obtain big catalytic current density, high efficiency and high production rate of formate were explored and discussed. Tin-Cu alloys with different compositions were characterized by morphology, structure, and composition. The electrochemical behavior of the alloys was measured by EIS and CV analysis. In addition, FE, partial current density (PCD), and concentration of HCOO− on the alloys were investigated and compared with those on Sn and Cu electrodes. References [1] S. Rasul, D.H. Anjum, A. Jedidi, Y. Minenkov, L. Cavallo, K. Takanabe, Angew. Chem. Int. Ed., 54,2146(2015). [2] Q. Lu, J. Rosen, Y. Zhou, G.S. Hutchings, Y.C. Kimmel, J.G. Chen, F. Jiao, Nat. Commun., 5,4242(2014). [3] Q. Shen, Z.F. Chen, X.F. Huang, M.C. Liu, G.H. Zhao, Environ. Sci. Technol., 49,5828(2015). [4] X.Q. Min, M.W. Kanan, J. Am. Chem. Soc., 137, 4701(2015).
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".