Immobilization of cobalt oxide nanoparticles on porous nitrogen-doped carbon as electrocatalyst for oxygen evolution

Highly efficient and robust electrocatalysts have been in urgent demand for oxygen evolution reaction(OER).For this purpose,high-cost carbon materials,such as graphene and carbon nanotubes,have been used as supports to metal oxides to enhance their catalytic activity.We report here a new Co_(3)O_(4)-based catalyst with nitrogen-doped porous carbon material as the support,prepared by pyrolysis of porous polyurea(PU) with Co(NO_(3))_(2)immobilized on its surface.To this end,PU was first synthesized,without any additive,through a very simple one-step precipitation polymerization of toluene diisocyanate in a binary mixture of H2O-acetone at room temperature.By immersing PU in an aqueous solution of Co(NO_(3))_(2)at room temperature,a cobalt coordinated polymer composite,Co(NO_(3))_(2)/PU,was obtained,which was heated at 500℃ in air for 2 h to get a hybrid,Co_(3)O_(4)/NC,consisting of Co_(3)O_(4)nanocrystals and sp2-hybridized N-doped carbon.Using this Co_(3)O_(4)/NC as a catalyst in OER,a current density of10 mA·cm^(-2)was readily achieved with a low overpotential of 293 mV with a Tafel slope of87 mV·dec^(-1),a high catalytic activity.This high performance was well retained after 1000 recycled uses,demonstrating its good durability.This work provides therefore a facile yet simple pathway to fabrication of a new transition metal oxides-based N-doped carbon catalyst for OER with high performance.

功能高分子开展“课程思政”教育的探索与实践

力求在教学内容不断拓展、教学方法不断丰富的同时,积极开发利用高分子专业课的思想政治教育资源,将思想政治与理论课程相融合,推进功能高分子课程思政教学改革。提出了功能高分子开展课程思政改革的紧迫性,指出该课程与课程思政元素的映射与融入点,阐述了将课程思政元素与理论课程教学相融合的教学方法。坚持知识传授与价值引领相结合,在功能高分子授课中融入培养大学生理想信念、价值取向、政治信仰、社会责任的题材与内容,使学生更加明确思想信念,提升心理素质,端正价值取向,增强社会责任感,为社会发展培养德才兼备、全面发展的高素质高分子人才。

Fluorescence Behavior and Emission Mechanisms of Poly(ethylene succinamide)and Its Applications in Fe^(3+)Detection and Data Encryption

Conventional fluorescent polymers are featured by large conjugation structures.In contrast,a new class of fluorescent polymers without any conjugations is gaining great interest in immerging applications.Polyamide is a typical member of the conjugation-free fluorescent polymers.However,studies on their electrophotonic property are hardly available,although widely used in many fields.Herein,poly(ethylene succinamide),PA24,is synthesized;its chemical structure confirmed through multiple techniques(NMR,FTIR,XRD,etc.).PA24 is highly emissive as solid and in its solution at room temperature,and the emission is excitation and concentration dependant,with an unusual blue shift under excitation from 270 nm to 320 nm,a hardly observed phenomenon for all fluorescent polymers.Quite similar emission behavior is also observed under cryogenic condition at 77 K.Its emission behavior is thoroughly studied;the ephemeral emission blue-shift is interpreted through Förster resonance energy transfer.Based on its structures,the emission mechanism is ascribed to cluster-triggered emission,elucidated from multianalyses(NMR,FTIR,UV absorbance and DLS).In presence of a dozen of competitive metal ions,PA24 emission at 450 nm is selectively quenched by Fe^(3+).PA24 is used as probe for Fe^(3+)and H_(2)O_(2) detections and in data encryption.Therefore,this work provides a novel face of polyamide with great potential applications as sensors in different fields.

COVID-19 mortality in ICUs associated with critical care staffing

To the Editor Coronavirus disease 2019(COVID-19)has been around for over a year since December 2019,and the global outlook is not optimistic.Because of its high transmissibility and high mortality,management of the COVID-19 pandemic has become a major challenge for health systems globally,especially for critical care.

Machine learning discovery of distinguishing laboratory features for severity classification of COVID-19 patients

The exponential spread of COVID-19 worldwide is evident,with devastating outbreaks primarily in the United States,Spain,Italy,the United Kingdom,France,Germany,Turkey and Russia.As of 1 May 2020,a total of 3,308,386 confirmed cases have been reported worldwide,with an accumulative mortality of 233,093.Due to the complexity and uncertainty of the pathology of COVID-19,it is not easy for front-line doctors to categorise severity levels of clinical COVID-19 that are general and severe/critical cases,with consistency.The more than 300 laboratory features,coupled with underlying disease,all combine to complicate proper and rapid patient diagnosis.However,such screening is necessary for early triage,diagnosis,assignment of appropriate level of care facility,and institution of timely intervention.A machine learning analysis was carried out with confirmed COVID-19 patient data from 10 January to 18 February 2020,who were admitted to Tongji Hospital,in Wuhan,China.A softmax neural network-based machine learning model was established to categorise patient severity levels.According to the analysis of 2662 cases using clinical and laboratory data,the present model can be used to reveal the top 30 of more than 300 laboratory features,yielding 86.30%blind test accuracy,0.8195 F1-score,and 100%consistency using a two-way patient classification of severe/critical to general.For severe/critical cases,F1-score is 0.9081(i.e.recall is 0.9050,and precision is 0.9113).This model for classification can be accomplished at a mini-second-level computational cost(in contrast to minute-level manual).Based on available COVID-19 patient diagnosis and therapy,an artificial intelligence model paradigm can help doctors quickly classify patients with a high degree of accuracy and 100%consistency to significantly improve diagnostic and classification efficiency.The discovered top 30 laboratory features can be used for greater differentiation to serve as an essential supplement to current guidelines,thus creating a more comprehensive assessment of COVID-19 cases during the early stages of infection.Such early differentiation will help the assignment of the appropriate level of care for individual patients.