Vinyl-functionalized mesoporous carbon for dispersive micro-solid phase extraction of azole antifungal agents from aqueous matrices
Bibliographic record
Abstract
Noorfatimah Yahaya*abc , Sazlinda Kamaruzamand , Mohd Marsin Sanagice, Wan Aini Wan Ibrahimce , Takahito Mitomef, Norikazu Nishiyamaf, Hadi Nure , Zainab Abdul Ghaffara, Mohd Yusmaidie Aziza & Hafizuddin Mohamed Fauziaa Integrative and Regenerative Medicine Clusters, Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, Penang, Malaysiab Department of Chemistry, University of British Columbia, Vancouver, BC, Canadac Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Bahru, Johor, Malaysiad Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, Serdang, Selangor, Malaysiae Ibnu Sina Institute for Fundamental Science Studies, Nanotechnology Research Alliance, Universiti Teknologi Malaysia, Bahru, Johor, Malaysiaf Division of Chemical Engineering, Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, JapanCONTACT Noorfatimah Yahaya noorfatimah@usm.my Integrative Medicine Cluster, Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, Bertam Kepala Batas, Penang 13200, MalaysiaColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/lsst.ABSTRACTA simple, rapid and sensitive vinyl-functionalized mesoporous carbon-based dispersive micro-solid phase extraction method has been developed for the preconcentration and quantification of azole antifungal drugs in aqueous matrices. The effects of type of adsorbent, desorption solvent, amount of adsorbent, pH, desorption time, salt addition and extraction time were investigated. Under the optimized conditions, the method demonstrated good linearity over the range of 1–300 µg L−1 for water sample and 5–400 µg L−1 for biological samples, low limits of detection (0.4 µg L−1 to 1.6 µg L−1), good analyte recoveries (89.8–113.9%) and acceptable RSDs (7.5–13.4%).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".