Enhanced sonocatalytic degradation of carbamazepine and salicylic acid using a metal-organic framework
Bibliographic record
Abstract
A metal-organic framework (MOF) was used as a sonocatalyst for ultrasonic (US) processes, to improve the degradation of two selected pharmaceutical active compounds (PhACs); carbamazepine (CBM) and salicylic acid (SA). The intrinsic characteristics of the MOF were characterized using a porosimeter (N2-BET) and scanning electron microscope (SEM). Various experiments were carried out under conditions with different US frequencies (28 and 1000 kHz), US power densities (45–180 W L−1), pH conditions (3.5, 7, and 10.5), and temperatures (293, 303, and 313 K) to investigate the degradation rates of the selected PhACs. Improved removal rates of PhACs were demonstrated within 60 min at 28 kHz (46% for SA; 47% for CBM) and 1000 kHz (60% for SA; 99% for CBM) with an MOF concentration of 45 mg L−1 in the US/MOF system, in comparison to 28 kHz (20% for SA; 25% for CBM) and 1000 kHz (37% for SA; 97% for CBM) under the ‘US only’ process. The removal of CBM was greater than that of SA under all experimental conditions due to the intrinsic properties of the PhACs. The degradation rates of PhACs are related to the quantity of H2O2; degradation is thus mostly affected by OH oxidation, which is generated by the dissociation of water molecules. The advantages of the ‘US/MOF system’ are as follows: (i) dispersion of MOF by US can improve sites and reactivity with respect to adsorption between the adsorbate (PhACs) and the adsorbent (MOF), and (ii) dispersed MOF acted as additional nuclei for water molecule pyrolysis, leading to the production of more OH. Therefore, based on the synergy indices, which were calculated using the removal rate constants [k1 (min−1)] of the pseudo-first order kinetic model, the ‘US/MOF system’ can potentially be used to treat organic pollutants (e.g., PhACs).
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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 | 0.000 | 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".