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Record W2013214640 · doi:10.1515/hf-2012-0136

Quantification of mobilized copper(II) levels in micronized copper-treated wood by electron paramagnetic resonance (EPR) spectroscopy

2013· article· en· W2013214640 on OpenAlexaff
Wei Xue, Pierre Kennepohl, J. N. R. Ruddick

Bibliographic record

VenueHolzforschung · 2013
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsElectron paramagnetic resonanceCopperChemistryAnalytical Chemistry (journal)SawdustNuclear chemistrySpectroscopyNuclear magnetic resonanceChromatography

Abstract

fetched live from OpenAlex

Abstract Sapwood sawdust from southern pine was treated with micronized copper (MC) under various conditions and the mobilized copper(II) (Cu mob ) concentrations were determined in the treated wood by electron paramagnetic resonance (EPR) spectroscopy. The spectral parameters for the copper sulfate (CuSO 4 )-treated sapwood and those of the MC-treated sapwood were very similar. A linear correlation was found between the intensities of copper (Cu) EPR spectra and those of Cu energy-dispersive X-ray fluorescence spectroscopy in a series of CuSO 4 -treated sapwood reference samples. Thus, the EPR signal intensities could be reliably correlated to the mass of reacted Cu present using this calibration curve. The amount of the Cu mob in sawdust treated by MC suspensions increased during the first 2–3 days after the initial treatment and then reached a maximum during the 7-day monitoring period. In the case of the treatment with MC alone or MC azole, an increased MC concentration led to an elevated amount of Cu (to a maximum of ∼0.23% Cu) solubilized by the sapwood. If the wood was treated with MC quat, the Cu mob initially increased, but at higher concentrations the Cu mob content decreased, due to the interference by the quat cobiocide on the acid reaction between the wood and the basic Cu carbonate. An examination of commercially-treated wood confirmed the laboratory observations.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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.

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

Citations9
Published2013
Admission routes1
Has abstractyes

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