MétaCan
Menu
Back to cohort
Record W4367182085 · doi:10.3390/su15097265

Biochar Prepared by Microwave-Assisted Co-Pyrolysis of Sewage Sludge and Cotton Stalk: A Potential Soil Conditioner

2023· article· en· W4367182085 on OpenAlexaff
Junshen Qu, Daiying Wang, Zeyu Deng, Hejie Yu, Jianjun Dai, Xiaotao Bi

Bibliographic record

VenueSustainability · 2023
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiocharPyrolysisStalkSewage sludgeNitrogenEnvironmental scienceAgronomyEnvironmental chemistrySoil conditionerChemistryPulp and paper industryCompostWaste managementSewageSoil waterEnvironmental engineeringHorticultureSoil scienceBiology

Abstract

fetched live from OpenAlex

The reduction and safe disposal of sewage sludge remains an urgent problem worldwide. In this work, biochar prepared from co−pyrolysis of sewage sludge and cotton stalk at different mix ratios and different pyrolysis temperatures was prepared using a novel microwave-assisted auger reactor. The obtained biochar samples were mixed with selected soil samples at different mix ratios for a short−term plant−growing test to examine their abilities as a soil conditioner on nitrogen fixation and retention. The addition of biochar could increase the total nitrogen in the soil to 0.3951% compared to 0.0403% in the untreated soil, while the concentration of available nitrogen could be increased to 114.45 mg·kg−1 compared to 47.95 mg·kg−1 in the untreated soil. Moreover, the introduction of biochar to the soil also contributed to the growth of corn seedlings, which grew at a rate of 3.41 cm·d−1 compared to 3.03 cm·d−1 in untreated soil. The results show that the addition of biochar can enrich total soil nitrogen before and after incubation and promote the growth of corn seedlings, providing a potential route for the safe disposal and resource recovery of sewage sludge.

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 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 score0.026
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.282
Teacher spread0.273 · 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.

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".

Quick stats

Citations12
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueSustainabilitySame topicClay minerals and soil interactionsFrench-language works237,207