(Invited) Ordered, Nanoporous Carbon Scaffolds (NCS) for Use in Energy Conversion and Related Applications
Bibliographic record
Abstract
Nanoporous carbon materials are of significant interest for use in various electrochemical applications, including as catalyst supports in fuel cells, sensors, and as electrodes in supercapacitors, redox flow batteries, and lithium batteries. The nanoporous carbons under study in our group include both soft- and hard-templated ordered mesoporous carbons as well as colloid-imprinted carbons (CICs). While all of these carbons have well-controlled and narrow pore size distributions and high surface areas, they are all produced in the form of powders, which can cause problems when used in electrochemical devices. This is due to challenges in relation to reproducible powder packing and binders must normally be used to hold particles together. The binder may then block ions and electrons from passing through the pores and between particles, thus preventing full utilization of their surfaces. Furthermore, fine particulates may also cause health concerns (e.g., PM2.5). In order to overcome these challenges, our research group has focussed on the development of a series of novel nanoporous carbon scaffolds (NCS membranes), possessing a fully ordered and monodisperse porous structure. The NCS materials have pore diameters in the range of 5 to 100’s of nm and contain a fully inter-connected nano/macro-porous network (porosity ~80%). These NCS membranes, which can be made to be 0.5 - 100’s of microns in thickness, also have a very good conductivity of 2-10 S/cm and are fully electrochemically accessible in aqueous solutions, exhibiting a capacitance of 40-100 F/g. The fabrication method used to form these novel NCS membranes is also environmentally friendly and has been demonstrated to be applicable for manufacturing at large scale. Due to these exceptional properties, the NCS materials are of great interest for use in a wide range of clean energy and water remediation products. In this presentation, the preparation and properties of these materials will be discussed, including the successful loading of the NCS membranes with catalytic metal nanoparticles and internal surface functionalization. An overview of the application of the NCS material in PEM fuel cells, redox flow batteries, capacitors, and in nanofiltration applications will also be presented.
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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) | 0.009 | 0.003 |
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".