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Record W4295221693 · doi:10.1002/cjce.24653

Molecular distillation of copaiba oleoresin—A clean process for diterpenes enrichment

2022· article· en· W4295221693 on OpenAlexvenueno aff
Cleyson de Souza Galúcio, Cibelem I. Benites, Rodney Alexandre Ferreira Rodrigues, Juliana O. Bahú, Nadia Gagliardi Khouri, Víktor Oswaldo Cárdenas Concha, Rubens Maciel Filho, Maria Regina Wolf Maciel

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSesquiterpenes and Asteraceae Studies
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado do AmazonasConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsOleoresinChemistryDistillationResidue (chemistry)DiterpeneChromatographyStereochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Copaiba oleoresin has sesquiterpenes and diterpenes with significant medicinal properties, including being antimicrobial, antiparasitic, and wound healing, among others. Thus, the objective of this work was to study the diterpene enrichment of the copaiba oleoresin from Copaifera officinalis via molecular distillation. Evaporator temperature (EVT) and feed flow rate ( Q ) were evaluated using an experimental design (2 2 with central point) considering the ratio of distillate and residue streams (DTR) as the response to optimize the residue recovery. EVT and Q were the main effects for diterpenes recovery, with the best experimental condition at 100°C (EVT) and 15 ml/min ( Q ) under 0.001 mbar, of which the residue stream had a diterpenes content of 99.25%. With the molecular distillation process, it was possible to add value to the copaiba streams, separating and purifying diterpenes with applicability in the biomedical and pharmaceutical industries since no solvent is used in this process (clean).

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 score0.072
Threshold uncertainty score0.238

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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicSesquiterpenes and Asteraceae StudiesFrench-language works237,207