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Record W4399901506 · doi:10.1007/s42773-024-00350-1

Production and modifications of biochar to engineered materials and its application for environmental sustainability: a review

2024· review· en· W4399901506 on OpenAlexaff
Gokulan Ravindiran, Sivarethinamohan Rajamanickam, Gorti Janardhan, Gasim Hayder, Avinash Alagumalai, Omid Mahian, Su Shiung Lam, Christian Sonne

Bibliographic record

VenueBiochar · 2024
Typereview
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiocharSustainabilityProduction (economics)Environmental scienceWaste managementEngineeringEconomicsEcologyPyrolysis

Abstract

fetched live from OpenAlex

Abstract Biochar, a carbon-rich material produced from biomass waste through thermal conversion, holds great environmental promise. This article offers a comprehensive overview of the various feedstocks used in biochar production, the different types of thermal degradation processes, biochar characterization, properties, modifications to engineered materials, and their applications in the environment. The quality of biochar, including surface area, pore size and volume, and functional group formation, is significantly influenced by the specific conditions under which thermal conversion takes place. Each of the diverse processes employed to produce biochar yields a distinct set of properties in the final product. In recent years, biochar has gained widespread recognition and utilization in diverse fields such as wastewater treatment, carbon sequestration, reduction of greenhouse gas emissions, biogas production, catalysis in biofuel industries, construction, and soil enhancement. In summary, biochar is a promising environmental mitigation tool to achieve a sustainable environment. In addition to its benefits, the application of biochar presents several challenges, including the selection of feedstocks, methods of biochar production, modifications to biochar, the properties of biochar, and the specific applications of biochar. The current review summarizes factors that could lead to significant advancements in future applications. Graphical Abstract

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.019
GPT teacher head0.269
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations153
Published2024
Admission routes1
Has abstractyes

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