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CO 2 -responsive Pickering emulsions stabilized by soft protein particles for interfacial biocatalysis.

Yongkang XiBo LiuShuxin WangShuheng WeiShou-Wei YinTo NgaiXiao-Quan Yang
Published in: Chemical science (2022)
Pickering emulsions are emulsions stabilized by colloidal particles and serve as an excellent platform for biphasic enzymatic catalysis. However, developing simple and green strategies to avoid enzyme denaturation, facilitate product separation, and achieve the recovery of enzyme and colloidal particle stabilizers is still a challenge. This study aimed to report an efficient and sustainable biocatalysis system via a robust CO 2 /N 2 -responsive Pickering oil-in-water (o/w) emulsion stabilized solely by pure sodium caseinate (NaCas), which was made naturally in a scalable manner. The NaCas-stabilized emulsion displayed a much higher reaction efficiency compared with conventional CO 2 /N 2 -responsive Pickering emulsions stabilized by solid particles with functional groups from polymers or surfactants introduced to tailor responsiveness, reflected by the fact that most enzymes were transferred and enriched at the oil-water interface. More importantly, the demulsification, product separation, and recycling of the NaCas emulsifier as well as the enzyme could be facilely achieved by alternatively bubbling CO 2 /N 2 more than 30 times. Moreover, the recycled enzyme still maintained its catalytic activity, with a conversion yield of more than 90% after each cycle, which was not found in any of the previously reported CO 2 -responsive systems. This responsive system worked well for many different types of oils and was the first to report on a protein-based CO 2 /N 2 -responsive emulsion, holding great promise for the development of more sustainable, green chemical conversion processes for the food, pharmaceutical, and biomedical industries.
Keyphrases
  • cancer therapy
  • drug delivery
  • machine learning
  • high throughput
  • small molecule
  • liquid chromatography
  • molecular dynamics simulations
  • big data
  • artificial intelligence
  • electron transfer