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Record W4413479062 · doi:10.1016/j.apsusc.2025.164406

Advanced nitrogen-doped wood-derived biocarbon for supercapacitor electrode applications

2025· article· en· W4413479062 on OpenAlexaff
Weipeng Zhang, Xijuan Zhang, Dexian Ji, Chuanyin Xiong, Yonghao Ni

Bibliographic record

VenueApplied Surface Science · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsSupercapacitorElectrodeDopingNitrogenMaterials scienceNanotechnologyChemical engineeringEnvironmental scienceAnalytical Chemistry (journal)OptoelectronicsChemistryCapacitanceEnvironmental chemistryEngineeringPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

This study presents a sustainable and scalable strategy for synthesizing N-doped porous carbon from rotten wood through a one-step green process. The natural fungal decay of wood forms a porous structure, which is crucial for the synthesis of porous carbon. Additionally, the decay process generates some nitrogen, which is further enhanced by introducing ethylenediamine (EDA) as an additional nitrogen source during carbonization. The resulting material exhibits a high specific surface area (1204 m 2 · g −1 ), excellent specific capacitance (448F · g −1 ), and remarkable cycle stability (95% retention after 10,000 cycles). These exceptional electrochemical properties highlight the potential of biomass-derived carbon materials for next-generation energy storage applications, offering an eco-friendly solution for converting waste into high-performance supercapacitor electrodes. • Taking advantage of the fungi on rotten wood, which produces a porous structure, we developed a scalable, green approach to convert it into N-doped porous carbon. • Nitrogen-doped carbon derived from rotten wood exhibited excellent supercapacitor performance. • The optimized sample, RW-1000, achieved a high specific surface area of 1204 m 2 · g −1 . • RW-1000-based electrode delivered a specific capacitance of 448F · g −1 at 0.2 A · g −1 . • Exceptional cycling stability was demonstrated, with 95% capacitance retention after 10,000 cycles. Biomass-derived carbon materials are receiving much attention for supercapacitor applications due to their well-developed porous structures, large specific surface areas, good conductivity, and environmental sustainability. In this study, we present a simple and scalable green strategy to prepare nitrogen (N)-doped biocarbon materials from naturally decayed wood (rotten wood, RW), which is to take advantage of the intrinsic porous nature of wood and the structural modifications induced by microbial activation (e.g., surface oxidation and nitrogen incorporation). To further increase the doped nitrogen content, we used aqueous ethylenediamine (EDA) solution for activating and immersing pre-treated RW. The N-doped biocarbon samples show uniform nitrogen distribution and favorable graphitization, resulting in outstanding supercapacitor performance. The optimized sample- RW-1000 exhibits a high specific surface area of 1204 m 2 · g −1 with a hierarchical porous structure. When applied as a supercapacitor electrode, RW-1000 demonstrates excellent electrochemical properties, including a specific capacitance of 448F · g −1 at 0.2 A · g −1 . The device assembled using this biocarbon delivers an energy density of up to 10.2 Wh · kg −1 at a power density of 100 W · kg −1 , while exhibiting excellent cycling stability with 95 % capacitance retention after 10,000 charge–discharge cycles. This exceptional energy storage performance is attributed to the RW-1000-derived electrode’s high specific surface area, optimal pore size distribution, and well-dispersed nitrogen content. Thus, our work offers a sustainable, scalable and facile strategy for transforming biomass waste into valuable biocarbon materials for high performance supercapacitors.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.254
Teacher spread0.243 · 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 teacher head, not a consensus.

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".

Quick stats

Citations9
Published2025
Admission routes1
Has abstractyes

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