Recent advances on hydrothermal carbonization of biomass for carbon-negative materials: From mechanistic insights to functional applications
Bibliographic record
Abstract
Under the combined pressures of global carbon neutrality goals and climate change, the development of technologies with negative carbon potential is of critical importance. Biomass, as a renewable, carbon-neutral, and abundant resource, holds great potential for carbon sequestration. Hydrothermal carbonization (HTC) is a mild thermochemical conversion process well-suited for wet biomass, enabling the efficient synthesis of structurally tunable carbon materials. However, the HTC process involves complex reaction mechanisms and spans multiple disciplines, posing ongoing challenges in precisely controlling reaction pathways. This review summarizes recent advances in the HTC of biomass for the synthesis of negative carbon materials. It systematically discusses the underlying reaction mechanisms and structural regulation strategies of HTC, and highlights its emerging applications in energy, environmental remediation, catalysis, and soil improvement. The review aims to offer insights and guidance for the efficient synthesis of high-performance negative carbon materials. • Summarizes recent advances in hydrothermal carbonization of biomass. • Explores HTC reaction mechanisms and structural regulation strategies. • Highlights HTC-derived carbon applications in energy and environment. • Provides insights for efficient synthesis of carbon-negative materials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".