Laccase-catalyzed oxidation of aqueous phenols at low concentrations
Notice bibliographique
Résumé
In this study, the feasibility of using laccase from Trametes versicolor to catalyze the oxidation of aqueous phenolic substrates at initial concentrations ranging from 0.1 to 50 micromolar was assessed. In particular, the oxidation of substrates of environmental concern including phenol, estradiol, cumylphenol and triclosan was tested experimentally over a wide range of initial concentrations, enzyme concentrations and as a function of time. Moreover, to provide a means for predicting the kinetics of reactions of such substrates at low concentrations, a semi-empirical kinetic model was developed based on the known reactions of the catalytic cycle of laccase. This model accounted for the influence of unproductive side reactions that become important when substrates are at low concentrations. The model was initially developed, calibrated and validated for batch reactions of phenol. Phenol was the slowest of the substrates studied and did not cause inactivation of the enzyme. It was shown that the model accurately predicted the time course of reactions over a wide range of phenol and enzyme concentrations, even for reactant concentration and reaction times that were far outside of the range of calibration of the model. The general applicability of the model was subsequently demonstrated by extending it to reactions of estradiol, cumylphenol and triclosan, each of which are characterized by very different reaction rates with laccase. It was shown that during the oxidation of these substrates, laccase is inactivated and, as such, a new term was incorporated into the model to account for the kinetics of inactivation. After calibrating the five kinetic parameters for each of the substrates, the general model demonstrated its ability to accurately predict the time course of batch reactions of all phenolic substrates for substrate and enzyme concentrations that varied over three orders of magnitude and for concentrations outside of the range of its calibration. The utility of the model was demonstrated by showing how it could be used to estimate the quantities of enzyme and reaction time required to achieve various levels of conversion of each substrate over a range of initial concentrations and also to achieve residual concentrations that would ensure the protection of aquatic life in surface waters. This work was extended further in order to evaluate the impacts of the presence of other substrates in a mixture would have on the oxidation of a substrate that was targeted for oxidation. In general, it was shown that (1) non-inactivating secondary substrates have a significant negative impact on the rate of oxidation of the target substrate if both are very fast substrates of laccase and, furthermore, this impact will increase with increasing concentration of the secondary substrate relative to the target substrate; and (2) inactivating secondary substrates,have important negative impacts on the oxidation of the target substrate and these impacts increase with its concentration. As part of this study, the kinetic model described above was adapted further to model reactions of mixtures of substrates. It was shown that, without further calibration of the kinetic parameters beyond that which had been done in earlier studies of reactions of single substrates, the multi-substrate model was generally able to accurately model the time course of reactions of mixtures of phenols. The exception to this were reactions that simultaneously involved estradiol and triclosan where an additional source of laccase inactivation occurred that was not accounted for by the model. The utility of the multi-substrate model was demonstrated by showing how it could be used to predict the quantities of enzyme and reaction times required to accomplish the oxidation of substrates targeted for oxidation in various mixtures of other substrates.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».