Degradation of PCDD/Fs in MSWI fly ash using a microwave-assisted hydrothermal process

In this work,microwave treatment was introduced to a hydrothermal treatment process to degrade PCDD/Fs(Polychlorinated dibenzo-p-dioxins and dibenzofurans)in municipal solid waste incineration(MSWI)fly ash.Three process additives(NaOH,Na2 HPO4,H2 O),temperatures(150℃,185℃,220℃)and reaction times(1 h,2 h,3 h)were investigated to identify their effect on the disposal of fly ash samples through orthogonal experiments.High-resolution gas chromatography–mass spectrometry(HRGC/MS)was applied to determine the PCDD/F concentrations in MSWI fly ash.The experimental results revealed that 83.7%of total PCDD/Fs was degraded.Reaction temperature was the most important factor for the degradation of the total PCDD/Fs.Both direct destruction and chlorination reactions(the chlorination degree of PCDFs increased)took part in the degradation of PCDD/Fs in fly ash,which was a new discovery.Several PCDD/F indexes determined by the concentration of indicative congeners were found to quantitatively characterize the dioxin toxicity of the fly ash.Furthermore,heavy metals in the fly ash sample were solidified using microwave-assisted hydrothermal treatment,which provided an experimental basis for the simultaneous disposal of dioxins and heavy metals.Thus,the microwave-assisted hydrothermal process should be considered for the future disposal of MSWI fly ash.

Exploration on learning molecular docking with deep learning models

Background:Molecular docking-based virtual screening(VS)aims to choose ligands with potential pharmacological activities from millions or even billions of molecules.This process could significantly cut down the number of compounds that need to be experimentally tested.However,during the docking calculation,many molecules have low affinity for a particular protein target,which waste a lot of computational resources.Methods:We implemented a fast and practical molecular screening approach called DL-DockVS(deep learning dock virtual screening)by using deep learning models(regression and classification models)to learn the outcomes of pipelined docking programs step-by-step.Results:In this study,we showed that this approach could successfully weed out compounds with poor docking scores while keeping compounds with potentially high docking scores against 10 DUD-E protein targets.A self-built dataset of about 1.9 million molecules was used to further verify DL-DockVS,yielding good results in terms of recall rate,active compounds enrichment factor and runtime speed.Conclusions:We comprehensively evaluate the practicality and effectiveness of DL-DockVS against 10 protein targets.Due to the improvements of runtime and maintained success rate,it would be a useful and promising approach to screen ultra-large compound libraries in the age of big data.It is also very convenient for researchers to make a well-trained model of one specific target for predicting other chemical libraries and high docking-score molecules without docking computation again.