Tailoring the Nanostructure of Colloidal Imprinted Carbons for Their Application in Electrochemical Devices
Bibliographic record
Abstract
Mesoporous colloid imprinted carbons (CIC) have many promising properties for use in various applications, including as catalyst supports in fuel cells, and in supercapacitors, batteries, sensors, and for water deionization. For example, in our past work, we have shown that Pt-loaded CICs exhibit very good activity for oxygen reduction in proton exchange membrane fuel cells (PEMFCs) [1, 2]. The CICs are typically prepared by imprinting a mesophase pitch powder with a colloidal silica template, carbonizing the imprinted pitch, and then removing the silica template [3-5]. The CICs are distinguished by their ordered spherical pores, having uniform diameters on the nanometer scale. The diameter of these CIC pores can be tuned during the synthesis by using silica colloids of controlled particle sizes (e.g., from several to hundreds of nanometers). This allows a wide variety of ordered pore sizes to be produced and studied. However, constrictions present between each spherical pore may limit ion transport. Theoretical calculations suggest that these pore “necks” are ca. 25-50% of the diameter of the corresponding spherical pores. The relatively small diameter of these pore necks can potentially hinder transport from one region to another, thus influencing the performance of CIC-based electrochemical devices when mass transport is limiting. In our work, we are focusing on how CIC nanostructuring, including controlling the pore neck size, influences electrochemical performance. Here, we report a novel approach to tuning the pore neck size, using a silica precursor to modify the colloidal silica templates prior to imprinting with mesophase pitch. By adjusting the concentration of the silica precursor solution, we can improve the accessibility of each spherical pore within the CICs. To characterize the modified CICs, nitrogen adsorption/desorption isotherms were collected to determine the surface area and pore size distribution, while FE-SEM was used to verify the size of the nanopores, particularly the diameter of the pore necks. Cyclic voltammetry and electrochemical impedance spectroscopy were then used to investigate the effect of pore neck size on mass transport through the CIC particles. References: [1] D. Banham, F. Feng, T. Fürstenhaupt, K. Pei, S. Ye, V. Birss, Novel Mesoporous Carbon Supports for PEMFC Catalysts, Catalysts, 5 (2015) 1046. [2] K. Pei, D. Banham, F. Feng, T. Fürstenhaupt, S. Ye, V. Birss, Oxygen reduction activity dependence on the mesoporous structure of imprinted carbon supports, Electrochemistry Communications, 12 (2010) 1666-1669. [3] B. Fang, J.H. Kim, J.S. Yu, Colloid-imprinted carbon with superb nanostructure as an efficient cathode electrocatalyst support in proton exchange membrane fuel cell, Electrochemistry Communications, 10 (2008) 659-662. [4] Z. Li, M. Jaroniec, Synthesis and adsorption properties of colloid-imprinted carbons with surface and volume mesoporosity, Chemistry of Materials, 15 (2003) 1327-1333. [5] Z. Li, M. Jaroniec, Colloidal imprinting: A novel approach to the synthesis of mesoporous carbons [2], Journal of the American Chemical Society, 123 (2001) 9208-9209.
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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 | 0.001 |
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".