Development and validation of a green RP-HPLC method for detection and quantitation of meropenem trihydrate in the bulk: A comparison with HPTLC method
Bibliographic record
Abstract
This research introduces an eco-friendly green Reverse-Phase High Performance Liquid Chromatography (RP-HPLC) method for detecting and quantifying meropenem trihydrate. Traditional methods use buffered solutions, gradient mobile phases, and longer retention times, but this method offers a rapid RP-HPLC technique with a C8 column and isocratic mobile phase (60% methanol, 40% ultra-pure water), eliminating the need for buffers and acetonitrile. It features a short 2.1-minute retention time and a high r² value of 0.9995. It delivers accuracy (101.1-102.3%) and precision (RSD ≤ 2%), coupled with low LOD and LOQ values of 1.72 and 5.20 µg/ml. Aqueous dilution simplifies sample preparation, reducing degradation and interference. The method is compared with an HPTLC method, showing an extended linear range (6.25-200 µg/ml for HPLC, 7.81-62.5 µg/ml for TLC) and high sensitivity, making it significant for meropenem trihydrate quality control in bulk and dosage forms.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".