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Record W2906594064 · doi:10.15376/biores.14.1.hubbe

Lignin recovery from spent alkaline pulping liquors using acidification, membrane separation, and related processing steps: A Review

2019· review· en· W2906594064 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBioResources · 2019
Typereview
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsNational Research Council CanadaFPInnovations
FundersLakehead UniversityNorth Carolina State University
KeywordsBiorefineryLigninBlack liquorSolubilityKraft paperPulp (tooth)ChemistryPulp and paper industryKraft processMembraneUltrafiltration (renal)Organic chemistryChemical engineeringChromatographyRaw materialEngineering

Abstract

fetched live from OpenAlex

The separation of lignin from the black liquor generated during alkaline pulping is reviewed in this article with an emphasis on chemistry. Based on published accounts, the precipitation of lignin from spent pulping liquor by addition of acids can be understood based on dissociation equilibria of weak acid groups, which affects the solubility behavior of lignin-related chemical species. Solubility issues also govern lignin separation technologies based on ultrafiltration membranes; reduction in membrane permeability is often affected by conditions leading to decreased solubility of lignin decomposition products and the presence of colloidal matter. Advances in understanding of such phenomena have potential to enable higher-value uses of black liquor components, including biorefinery options, alternative ways to recover the chemicals used to cook pulp, and debottlenecking of kraft recovery processes.

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.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
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)0.0000.000

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 head0.042
GPT teacher head0.303
Teacher spread0.261 · 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