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Record W2093483812 · doi:10.1039/b106639k

Evaluation of column temperature as a means to alter selectivity in the cation exchange separation of alkali metals, alkaline earth metals and amines

2001· article· en· W2093483812 on OpenAlexafffund
Panos Hatsis, Charles A. Lucy

Bibliographic record

VenueThe Analyst · 2001
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsSelectivityChemistryAlkaline earth metalAlkali metalAcetonitrileInorganic chemistryElutionAnalyteChromatographyCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

The merits of varying column temperature in a cation exchange separation of alkali metals, alkaline earth metals and amines are considered. Increasing the column temperature (up to 60 degrees C) reduced the retention of all cations, but by varying extents. Consequently, selectivity changes were seen, with reversals in elution order in some cases. To ascertain when temperature is most useful as a separation aid, analytes were classed into three groups according to their temperature behaviour: alkali metals; alkaline earth metals; and amines. Adjusting the column temperature caused selectivity changes between analytes in different groups, but no selectivity changes occurred between analytes in the same group. Further, temperature was compared to the addition of modest amounts of acetonitrile as another means to alter selectivity. The benefits of elevated temperature were not just limited to selectivity changes. Improvements in the efficiencies of all analytes were noted at 60 degrees C. This was especially true for the amines which are severely tailed at ambient temperatures.

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 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 score0.027
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.317
Teacher spread0.290 · 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

Citations31
Published2001
Admission routes2
Has abstractyes

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