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Record W2163944899 · doi:10.1515/hf.2007.040

Vapor-phase diethyl oxalate pretreatment of wood chips: Part 1. Energy savings and improved pulps

2007· article· en· W2163944899 on OpenAlexaboutno aff
William R. Kenealy, Eric Horn, Carl J. Houtman

Bibliographic record

VenueHolzforschung · 2007
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsOxalic acidOxalatePulp and paper industryMaterials sciencePinus <genus>LigninSoftwoodChemistryComposite materialBotanyOrganic chemistryBiologyEngineering

Abstract

fetched live from OpenAlex

Abstract Diethyl oxalate (DEO) was injected into a digester containing wood chips (pine, spruce, or aspen) preheated to 130–140°C and held for 30 min at the same temperature. When mechanical pulps were produced from these pretreated chips, savings in electrical refiner energy could be achieved. For southern yellow pine (Pinus taeda), the electrical refiner energy required to produce pulps with Canadian standard freeness (CSF) of 100 ml was 38– 55% less than for an untreated control. Energy savings were also observed for aspen (Populusspp.) and spruce (Piceaspp.). DEO-treated pine handsheets showed a 26% improvement in tear index compared with a control at 100 ml CSF. Under the pretreatment conditions de-scribed, DEO rapidly vaporizes, reacts with water already present in the chip, and forms an oxalic acid (OA) solution. Reported handsheet properties and energy savings for pine chips treated with a solution of OA are similar to DEO-treated handsheets. It can be concluded that the effect of DEO treatment is due to reactions catalyzed by OA.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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 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

Citations41
Published2007
Admission routes1
Has abstractyes

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