Hydrochar: A Review on Its Production Technologies and Applications
Bibliographic record
Abstract
Recently, due to the escalating usage of non-renewable fossil fuels such as coal, natural gas and petroleum coke in electricity and power generation, and associated issues with pollution and global warming, more attention is being paid to finding alternative renewable fuel sources. Thermochemical and hydrothermal conversion processes have been used to produce biochar and hydrochar, respectively, from waste renewable biomass. Char produced from the thermochemical and hydrothermal decomposition of biomass is considered an environmentally friendly replacement for solid hydrocarbon materials such as coal and petroleum coke. Unlike thermochemically derived biochar, hydrochar has received little attention due to the lack of literature on its production technologies, physicochemical characterization, and applications. This review paper aims to fulfill these objectives and fill the knowledge gaps in the literature relating to hydrochar. Therefore, this review discusses the most recent studies on hydrochar characteristics, reaction mechanisms for char production technology such as hydrothermal carbonization, as well as hydrochar activation and functionalization. In addition, the applications of hydrochar, mainly in the fields of agriculture, pollutant adsorption, catalyst support, bioenergy, carbon sequestration, and electrochemistry are reviewed. With advancements in hydrothermal technologies and other environmentally friendly conversion technologies, hydrochar appears to be an appealing bioresource for a wide variety of energy, environmental, industrial, and commercial applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".