Adsorption and the subsequent desorption of lactic acid molecules on zirconium metal‐organic frameworks: An innovative and efficient approach for the recovery of the produced lactic acid from fermentation broth
Bibliographic record
Abstract
Abstract Nowadays, biomass‐derived lactic acid serves as a significant foundational chemical in the pursuit of sustainable production of various materials. Nevertheless, the current fermentation process faces limitations due to the challenging retrieval of the lactic acid product from the fermentation broth, resulting in the production of gypsum waste in stoichiometric quantities. In this work, we demonstrate the efficacy of Zr‐UiO‐66 metal–organic frameworks (MOFs) as effective adsorbents in the process of recovery of lactic acid from the fermentation broth. The MOF beads have been manufactured successfully and were applied to recover lactic acid from the solution and the experimental conditions were optimized. The results show that Zr‐UiO‐66 adsorbent has a good ability of up to 98.8% to adsorb the produced lactic acid from its fermentation broth at a pH value of 6.5, adsorbent (MOF) dosage of 0.625 g, an initial concentration of 15 mg/L, temperature of 298 K, within process time of 4 h. The data obtained from the adsorption process demonstrated a satisfactory fit with the Langmuir isotherm model (with a R2 of 0.958), kinetics (with a R2 of 0.970), and thermodynamic results, confirming the spontaneous and exothermic nature of the adsorption process. The regeneration experiment of the MOF beads showed that the adsorption efficiency of Zr‐UiO‐66 remained above 90% even after undergoing seven cycles.
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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) | 0.000 | 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".