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Effect of the thermal modification and nano-ZnO impregnation on the deterioration of Caribbean pine wood

2020· article· es· W3080434060 on OpenAlexfundno aff
Bruno Couto da Silva, Henrique Trevisan, R. Á. Garcia

Bibliographic record

VenueMaderas Ciencia y tecnología · 2020
Typearticle
Languagees
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersDivision of Graduate EducationCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversité Laval
KeywordsMaterials scienceAutoclaveNano-Composite materialAqueous solutionZincThermalMetallurgyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

This study aimed to investigate the effect of thermal modification and nano-zinc oxide (nano-ZnO) particle impregnation on the deterioration of Caribbean pine wood under field conditions. Samples were thermallymodified at various temperature levels (control, 180 C, 200 C, and 220 C). Nano-ZnO impregnation was done with an aqueous solution at 1,5 % in an autoclave under two-steps of pressure and vacuum. Unmodified and thermally-modified, non-impregnated and nano-ZnO impregnated samples were exposed to deterioration for five months in field tests. A deterioration index was used to evaluate the health condition of the samples. The mass loss and occurrence of termite tunnels in percentage were also determined. The nano-ZnO impregnation improved the resistance of unmodified wood to field-deterioration. The thermal modification at 180 o C, and 200C increased the wood deterioration and nano-ZnO impregnation did not improve their resistance. Unmodified and 220 C modified samples had a lower mass loss by xylophages than other thermal treatments regardless of the nanoparticle impregnation. The nano-ZnO impregnation decreases the occurrence of termite tunnels in unmodified, 200 C and 220 C-modified samples.

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 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 score0.043
Threshold uncertainty score0.408

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 head0.015
GPT teacher head0.210
Teacher spread0.194 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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