Phosphorous and selenium tuning Co-based non-precious catalysts for electrosynthesis of H_(2)O_(2)in acidic media

Electrosynthesis of hydrogen peroxide(H2O2)is an on-site method that enables independent distribution applications in many fields due to its small-scale and sustainable features.The crucial point remains developing highly active,selective and cost-effective electrocatalysts.The electrosynthesis of H2O2 in acidic media is more practical owing to its stability and no need for further purification.We herein report a phosphorus and selenium tuning Co-based non-precious catalyst(CoPSe)toward two-electron oxygen reduction reaction(2e–ORR)to produce H2O2 in acidic media.The starting point of using both P and Se is finding a balance between strong ORR activity of CoSe and weak activity of CoP.The results demonstrated that the CoPSe catalyst exhibited the optimized 2e–ORR activity compared with CoP and CoSe.It disclosed an onset potential of 0.68 V and the H2O2 selectivity 76%-85%in a wide potential range(0–0.5 V).Notably,the CoPSe catalyst overcomes a significant challenge of a narrow-range selectivity for transitionmetal based 2e–ORR catalysts.Finally,combining with electro-Fenton reaction,an on-site system was constructed for efficient degradation of organic pollutants.This work provides a promising non-precious Co-based electrocatalyst for the electrosynthesis of H2O2 in acidic media.

Ultrafine Co nanoparticles confined in nitrogen-doped carbon toward two-electron oxygen reduction reaction for H_(2)O_(2) electrosynthesis in acidic media

Electrocatalytic production of hydrogen peroxide(H_(2)O_(2))by two-electron oxygen reduction reaction(2e^(-)ORR)under acidic condition has been considered to have great application value.Co nanoparticles(CoNPs)coupled with N-doped carbon are a class of potential electrocatalysts.The effective strategies to further enhance their performances are to improve the active sites and stability.Herein,the material containing ultrafine CoNPs confined in a nitrogen-doped carbon matrix(NC@CoNPs)was synthesized by pyrolyzing corresponding precursors,which was obtained through regulating the topological structure of ZIF-67/ZIF-8 with dopamine(DA).The DA self-polymerization process induced the formation of CoNPs with smaller sizes and formed polydopamine film decreased the detachment of CoNPs from the catalyst.High density of Co-N_(x) active sites and defective sites could be identified on NC@CoNPs,leading to high activity and H_(2)O_(2) selectivity,with an onset potential of 0.57 V(vs.RHE)and∼90%selectivity in a wide potential range.An on-site electrochemical removal of organic pollutant was achieved rapidly through an electro-Fenton process,demonstrating its great promise for on-site water treatment application.

Occupational Mercury Exposure at a Thermometer Facility-Jiangsu Province, 2019

Summary What is already known about this topic?Mercury is still used in the manufacture of some thermometers in China.This may pose health risks if exposure is not properly prevented and controlled.What is added by this report?An onsite investigation of a workplace at a thermometer facility in Jiangsu Province in 2019 found heavily elevated airborne and urinary mercury levels among a massive number of workers exposed to mercury.

Cost-effective method for degradability identification of MSW using convolutional neural network for on-site composting

Automatically identifying the degradability of municipal solid waste(MSW)is one of the key prerequisites for on-site composting to prevent contaminations from undegradable wastes.In this study,a cost-effective method was proposed for the degradability identification of MSW.Firstly,the trainable images in the datasets were increased by performing four different sizes of cropping operations on the original images captured on-site.Secondly,a lite convolutional neural network(CNN)model was built with only 3.37 million parameters,and then a total of eight models were trained on these datasets with and without the image augmentation operations,respectively.Finally,a degradability identification system was built for on-site composting,where the images were cut to different sizes of small squares for prediction,and the experiments were conducted to find the best combinations of the trained models and the cutting size.The results showed that the validation accuracies of the models trained with the augmentation operations were 0.91-2.07 percentage points higher,and in the evaluation of the degradability identification system the best result was achieved by the combination of W8A dataset and cutting size of 1/14 reached an accuracy of 91.58%,which indicated the capability of this cost-effective method to identify the degradability of MSW.

Method for C/N ratio estimation using Mask R-CNN and a depth camera for organic fraction of municipal solid wastes

Fast assessment of the initial carbon to nitrogen ratio(C/N)of organic fraction of municipal solid waste(OFMSW)is an important prerequisite for automatic composting control to improve efficiency and stability of the bioconversion process.In this study,a novel approach was proposed to estimate the C/N of OFMSW,where an instance segmentation model was applied to predict the masks for the waste images.Then,by combining the instance segmentation model with the depth-camera-based volume calculation algorithm,the volumes occupied by each type of waste were obtained,therefore the C/N could be estimated based on the properties of each type of waste.First,an instance segmentation dataset including three common classes of OFMSW was built to train mask region-based convolutional neural networks(Mask R-CNN)model.Second,a volume measurement algorithm was proposed,where the measurement result of the object was derived by accumulating the volumes of small rectangular cuboids whose bottom area was calculated with the projection property.Then the calculated volume was corrected with linear regression models.The results showed that the trained instance segmentation model performed well with average precision scores AP_(50)=82.9,AP_(75)=72.5,and mask intersection over unit(Mask IoU)=45.1.A high correlation was found between the estimated C/N and the ground truth with a coefficient of determination R2=0.97 and root mean square error RMSE=0.10.The relative average error was 0.42%and the maximum error was only 1.71%,which indicated this approach has potential for practical applications.