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Cluster adsorption of histidine enantiomers on carbon nanotubes from aqueous solutions

2024· article· en· W4400494540 on OpenAlexaboutno aff
E. V. Butyrskaya, Dinh Tuan Le, Alexander A. Volkov

Bibliographic record

VenueСорбционные и хроматографические процессы · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsnot available
Fundersnot available
KeywordsAqueous solutionCarbon nanotubeAdsorptionEnantiomerHistidineCluster (spacecraft)ChemistryChemical engineeringMaterials scienceNanotechnologyOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

The study is devoted to the analysis of adsorption isotherms of histidine enantiomers on carbon nanotubes mkNANO MKN-SWCNT S1 from aqueous solutions in the temperature range 25-80°C. Histidine enantiomers from Sigma Aldrich were used as amino acids, and mkNANO MKN-SWCNT S1 carbon nanotubes (Canada) were used as an adsorbent. Experimental data obtained by the construction of isotherms, were used to calculate the separation coefficients of enantiomers on CNTs, the values of separation coefficients were higher than for other sorbents. The interpretation of the isotherms was based on the cluster adsorption model, providing very good agreement between theory and experiment (R2=0.994-0.999), which showed that L- and D-histidine were sorbed on the surface of a nanotube in the form of monomers and clusters. Three characteristic regions were identified on the adsorption isotherms: concentration range in which only sorbate monomers were fixed on the surface of the nanotube; concentration range in which sorbate molecules were fixed on the nanotube only in the form of clusters; concentration range in which monomers and clusters were present on the surface of the sorbent. The dependence of these isotherm regions on temperature was analysed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.252
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

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
Published2024
Admission routes1
Has abstractyes

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