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Record W4247390070 · doi:10.1149/ma2017-01/37/1728

An Investigation into Capacitive Performances and Impedance Contributions Due to Structural Differences of Biochar Thin Film and Monolith Supercapacitor Electrodes

2017· article· en· W4247390070 on OpenAlexaff
Daniel Yanchus, Donald W. Kirk, Charles Q. Jia

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceBiocharSupercapacitorPyrolysisElectrodeCapacitive deionizationCarbon fibersPorosityNanotechnologyThin filmGrapheneCapacitive sensingMonolithEnergy storageElectrical conductorComposite materialChemical engineeringCapacitanceElectrochemistryComposite numberComputer sciencePower (physics)ChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Supercapacitors are an upcoming, high power density energy storage technology. Unlike in batteries, energy is stored physically through the adsorption of oppositely charged ions to a surface. Electrode materials that facilitate this process have high conductivity, extensive porosity, and high specific surface area. Porous carbon powders are used commercially, being pressed into thin films and held together with a binder material [1]. While carbon nanotubes and graphene are the focus of many researchers in this field, our group is investigating a unique alternative: biochar. Biochar is pyrolyzed biomass, and our group uses different types of wood as precursor materials. Through a controlled pyrolysis process, it is possible to preserve the internal macrostructures of wood, creating pathways throughout the carbon structure that should facilitate ion transport. By creating large, continuous monolithic pieces of carbon, devices can be constructed differently from the status quo. Using monolithic slices as electrodes simplifies the manufacturing process, reduces the number of ancillary components required per device, eliminates the need for a non-conductive binder material, and enables the construction of larger electrodes. Currently there is a lack of understanding regarding the relationships between biochar macrostructures and capacitive performance. While monolithic biochar electrodes were found to show similar capacitive performance to their thin film counterparts in [2], the study did not include an evaluation from a structural point of view. Additionally, although it was found that increasing electrode thickness of thin films resulted in an increase in device resistance [3], this relationship has not been explored for monolithic electrodes nor at a relevant scale, as it becomes possible to make electrodes hundreds of times thicker than those currently used [1]. Competitive capacitive performance of powdered biochar thin film electrodes compared to thin film alternatives has been demonstrated in [4] and [5]. The overall goal of this project is to determine if monolithic biochar electrodes can compete with the capacitive performance of powdered biochar thin film electrodes. By constructing monolithic slices and powdered thin films from the same biochar, electrochemical influences of the macrostructure are investigated. Capacitive performance metrics such as charge/discharge rate capability and self-discharge rates are explored for the two electrode structures, as well as for different electrode thicknesses. Frequency-dependent resistances and their respective contributions to total device resistance are analyzed through Electrical Impedance Spectroscopy (EIS). Mass transfer and diffusional resistances, which are believed to be highly dependent on both electrode geometry and structure will be reported. Characterization of the electrode materials using N2 and CO2 physisorption; helium pycnometry; and SEM imaging coincides with the electrochemical testing methods to help resolve the causes of performance differences and evaluate their significance. Preliminary results show that monolithic biochar electrodes up to 1mm thick have comparable specific capacitance performance to the thin films at 100mA/g, which was the highest current density employed thus far. Upcoming work will apply larger current densities to determine if the channels of the macrostructures contribute to high power performance or reduce volumetric capacitance. Monolithic electrodes up to 5mm thick are able to achieve similar specific capacitances to the thin films at a low current density (5mA/g), but their performance degrades significantly with charge rate. The experiments mentioned in the preceding paragraphs were all conducted on biochar from sugar maple wood. Research direction for early 2017 will involve analysis on biochar from different types of precursor wood. Soft and hard woods, and the variety of species within these categories have vastly different types and sizes of internal structures (Figure) [6]. The effects of these macrostructures on capacitive performance and ion transport will be assessed, and these results will be available for presentation. References [1] Wang Q, Yan J, Fan Z. Carbon materials for high volumetric performance supercapacitors: design, progress, challenges and opportunities. Energy and Environmental Science. 2016;9(3):729-62. [2] Zhang L, Jiang J, Holm N, Chen F. Mini-chunk biochar supercapacitors. Journal of Applied Electrochemistry. 2014;44:1145-51. [3] Yamada Y, Sasaki T, Tatsuda N, Weigarth D, Yano K, Kotz R. A novel model electrode for investigating ion transport inside pores in an electrical double-layer capacitor: monodispersed microporous starburst carbon spheres. Electrochimica Acta. 2012;81, 138–148. [4] Dehkhoda AM, Ellis N, Gyenge E. Electrosorption on activated biochar: effect of thermo-chemical activation treatment on the electric double layer capacitance. Journal of Applied Electrochemistry. 2014;44:141–157. [5] Jiang J, Zhang L, Wang X, Holm N, Rajagopalan K, Chen F, et al. Highly ordered macroporous woody biochar with ultra-high carbon content as supercapacitor electrodes. Electrochimica Acta. 2013;113:481-9. [6] Panshin AJ, Zeeuw Cd. Textbook of Wood Technology. 4th ed. New York: McGraw-Hill; 1980. Figure 1

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.261
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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