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Record W4381511238 · doi:10.5267/j.ccl.2023.6.001

Batch and merging-zone flow injection methods for determination of tetracycline hydrochloride

2023· article· en· W4381511238 on OpenAlexvenueno aff
Esraa Rasool Radhi, Khdeeja Jabbar Ali, Fatima Hydar Abdul Hussein

Bibliographic record

VenueCurrent Chemistry Letters · 2023
Typearticle
Languageen
FieldChemistry
TopicAnalytical Methods in Pharmaceuticals
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryPotassium permanganateAbsorbanceTetracyclineChromatographyTetracycline HydrochlorideCalibration curvePermanganateFlow injection analysisDosage formSpectrophotometryDetection limitOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

The objective of the present work is to develop batch and merging-zone flow injection methods for sensitive and accurate spectrophotometric determination of tetracycline hydrochloride. The methods depend on the oxidation of the studied drug with potassium permanganate in an alkaline medium, and the absorbance of the green oxidation product was measured at 610 nm. The calibration graphs in both procedures were linear in the concentration ranges of 0.5 – 25 and 1 – 25 μg mL−1 using the spectrophotometric and merging-zone flow injection methods, respectively. Specific and molar absorption coefficients, limits of detection and quantification, and Sandell’s sensitivity were calculated. The suggested procedures were further applied to the quantitative determination of tetracycline in pharmaceutical formulations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.058
GPT teacher head0.435
Teacher spread0.377 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations3
Published2023
Admission routes1
Has abstractyes

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