首页|New Findings from Commonwealth Scientific and Industrial Research Organisation (CSIRO) in the Area of Photocatalytics Reported (Machine-learning Assisted Optimisation During Heterogeneous Photocatalytic Degradation Utilising a Static Mixer…)
New Findings from Commonwealth Scientific and Industrial Research Organisation (CSIRO) in the Area of Photocatalytics Reported (Machine-learning Assisted Optimisation During Heterogeneous Photocatalytic Degradation Utilising a Static Mixer…)
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NETL
NSTL
Royal Soc Chemistry
Researchers detail new data in Nanotechnology-Photocatalytics. According to news reporting from Clayton, Australia, by NewsRx journalists, research stated, “A method for process optimisation using Bayesian optimisation (BO) in combination with a continuous flow photoreactor is presented. The photodegradation of an azo dye, as a proof-of-concept model reaction, using a novel TiO2 coated catalytic static mixer (CSM) was optimised using this BO method.” The news correspondents obtained a quote from the research from Commonwealth Scientific and Industrial Research Organisation (CSIRO), “The optimal temperature and flow rate were found after conducting 17 experimental runs, with an overall experiment run time of 21 hours. With full automation of the reactor into a closed loop system, this optimisation process can be carried out in under one day with almost no human intervention. Importantly, the algorithm presented successfully accounts for the challenges of catalyst degradation during processing.” According to the news reporters, the research concluded: “Machine-learning assisted optimisation of a continuous photodegradation reaction, using a TiO2 coated catalytic static mixer successfully accounting for catalyst degradation.” This research has been peer-reviewed.
ClaytonAustraliaAustralia and New ZealandCyborgsEmerging TechnologiesMachine LearningNanotechnologyPhotocatalystPhotocatalyticsCommonwealth Scientific and Industrial Research Organisation (CSIRO)
NETL
NSTL
Royal Soc Chemistry
