Miniaturized electrochemical sensors and their point-of-care applications

Most of the current analytical methods depend largely on laboratory-based analytical techniques that require expensive and bullky equipment,potentially incur costly testing,and involve lengthy detection processes.With increasing requirements for point-of-care testing(POCT),more attention has been paid to miniaturized analytical devices.Miniaturized electrochemical(MEC)sensors,including different material-based MEC sensors(such as DNA-,paper-,and screen electrode-based),have been in strong demand in analytical science due to their easy operation,portability,high sensitivity,as well as their short analysis time.They have been applied for the detection of trace amounts of target through measuring changes in electrochemical signal,such as current,voltage,potential,or impedance,due to the oxidation/reduction of chemical/biological molecules with the help of electrodes and electrochemical units.MEC sensors present great potential for the detection of targets including small organic molecules,metal ions,and biomolecules.In recent years,MEC sensors have been broadly applied to POCT in various fields,including health care,food safety,and environmental monitoring,owing to the excellent advantages of electrochemical(EC)technologies.This review summarized the state-of-the-art advancements on various types of MEC sensors and their applications in POCT.Furthermore,the future perspectives,opportunities,and challenges in this field are also discussed.     

Machine Learning towards Screening Solid-state Lithium Ion Conductors

Machine learning is an emerging method to discover new materials with specific characteristics.An unsupervised machine learning research is highlighted to discover new potential lithium ionic conductors by screening and clustering lithium compounds,providing inspirations for the development of solid-state electrolytes and practical batteries.     

Surface defect-rich ceria quantum dots anchored on sulfur-doped carbon nitride nanotubes with enhanced charge separation for solar hydrogen production

Designing defect-engineered semiconductor heterojunctions can effectively promote the charge carrier separation.Herein,novel ceria(CeO2) quantum dots(QDs) decorated sulfur-doped carbon nitride nanotubes(SCN NTs) were synthesized via a thermal polycondensation coupled in situ depositionprecipitation method without use of template or surfactant.The structure and morphology studies indicate that ultrafine CeO2 QDs are well distributed inside and outside of SCN NTs offering highly dispersed active sites and a large contact interface between two components.This leads to the promoted formation of rich Ce³⁺ ion and oxygen vacancies as confirmed by XPS.The photocatalytic performance can be facilely modulated by the content of CeO2 QDs introduced in SCN matrix while bare CeO2 does not show activity of hydrogen production.The optimal catalyst with 10% of CeO2 loading yields a hydrogen evolution rate of 2923.8 μmol h-1 g-1 under visible light,remarkably higher than that of bare SCN and their physical mixtures.Further studies reveal that the abundant surface defects and the created 0 D/1 D junctions play a critical role in improving the separation and transfer of charge carriers,leading to superior solar hydrogen production and good stability.     

Superfast and solvent-free core-shell assembly of sulfur/carbon active particles by hail-inspired nanostorm technology for high-energy-density Li-S batteries

The demand on low-carbon emission fabrication technologies for energy storage materials is increasing dramatically with the global interest on carbon neutrality.As a promising active material for metal-sulfur batteries,sulfur is of great interest due to its high-energy-density and abundance.However,there is a lack of industry-friendly and low-carbon fabrication strategies for high-performance sulfur-based active particles,which,however,is in critical need by their practical success.Herein,based on a hail-inspired sulfur nano-storm(HSN)technology developed in our lab,we report an energy-saving,solvent-free strategy for producing core-shell sulfur/carbon electrode particles(CNT@AC-S)in minutes.The fabrication of the CNT@AC-S electrode particles only involves low-cost sulfur blocks,commercial carbon nanotubes(CNT)and activated carbon(AC)micro-particles with high specific surface area.Based on the above core-shell CNT@AC-S particles,sulfur cathode with a high sulfur-loading of 9.2 mg cm^-2 delivers a stable area capacity of 6.6 mAh cm^-2 over 100 cycles.Furthermore,even for sulfur cathode with a super-high sulfur content(72 wt%over the whole electrode),it still delivers a high area capacity of 9 mAh cm^-2 over50 cycles in a quasi-lean electrolyte condition.In a nutshell,this study brings a green and industryfriendly fabrication strategy for cost-effective production of rationally designed S-rich electrode particles.     

Unraveling the stabilization mechanism of solid electrolyte interface on ZnSe by rGO in sodium ion battery

Transition metal selenides have been widely studied as anode materials of sodium ion batteries(SIBs),however,the investigation of solid-electrolyte-interface(SEI)on these materials,which is critical to the electrochemical performance of SIBs,remains at its infancy.Here in this paper,ZnSe@C nanoparticles were prepared from ZIF-8 and the SEI layers on these electrodes with and without reduced graphene oxide(rGO)layers were examined in details by X-ray photoelectron spectroscopies at varied charged/discharged states.It is observed that fast and complicated electrolyte decomposition reactions on ZnSe@C leads to quite thick SEI film and intercalation of solvated sodium ions through such thick SEI film results in slow ion diffusion kinetics and unstable electrode structure.However,the presence of rGO could efficiently suppress the decomposition of electrolyte,thus thin and stable SEI film was formed.ZnSe@C electrodes wrapped by rGO demonstrates enhanced interfacial charge transfer kinetics and high electrochemical performance,a capacity retention of 96.4%,after 1000 cycles at 5 A/g.This study might offer a simple avenue for the designing high performance anode materials through manipulation of SEI film.     

CXN天然沸石的研究2: 吸附性质

采用N~2,NH~3,CO~2,乙烯,丙烯,水,甲醇,乙醇,丙醇等作为吸附剂,研究了由我国CXN天然沸石改性制得的H-STI和Na-STI沸石的吸附性质,H-STI和Na-STI沸石的BET表面积及微孔孔体积约为420m^2/g和0.20m^3/g。根据NH~3和CO~2在H-STI沸石上的吸附等温线计算得到它们的吸附热分别为44.8和26.5kJ/mol。乙烯,丙烯,甲醇,乙醇,丙醇等在Na-STI沸石上的吸附等温线表明该沸石对有机分子的吸附具有链长选择性。在低分压下水相对于甲醇的吸附量表明沸石具有一定的疏水性质。     

常温常湿条件下Au/MeO~x催化剂上CO氧化性能

利用共沉淀法制备了Au/MeO~x催化剂(Me=Al,Co,Cr,Cu,Fe,Mn,Ni,Zn)。在常温常湿条件下,考察了不同氧化物负载的金基催化剂的CO氧化性能。结果表明,氧化物种类对催化剂的活性和稳定性均有较大的影响。Cu,Mn,Cr等氧化物负载的金基催化剂的活性较差,而Zn,Fe,Co,Ni,Al等金属氧化物负载的金基催化剂可将CO完全氧化,又具有一定的稳定性,在相同反应条件下,CO完全转化时的稳定性顺序为Au/ZnO>Au/α-Fe~2O~3>Au/Co~3O~4>Au/γ-Al~2O~3≈Au/NiO。还发现水对Au/MnO~x催化剂的活性和稳定性有负作用,而对180℃焙烧制备的Au/ZnO-180催化剂的活性和稳定性均有明显的湿度增强作用。     

Boost oxygen reduction reaction performance by tuning the active sites in Fe-N-P-C catalysts

Cost-effective atomically dispersed Fe-N-P-C complex catalysts are promising to catalyze the oxygen reduction reaction(ORR)and replace Pt catalysts in fuel cells and metal-air batteries.However,it remains a challenge to increase the number of atomically dispersed active sites on these catalysts.Here we report a highly efficient impregnation-pyrolysis method to prepare effective ORR electrocatalysts with large amount of atomically dispersed Fe active sites from biomass.Two types of active catalyst centers were identified,namely atomically dispersed Fe sites and Fe_xP particles.The ORR rate of the atomically dispersed Fe sites is three orders of magnitude higher than it of Fe_xP particles.A linear correlation between the amount of the atomically dispersed Fe and the ORR activity was obtained,revealing the major contribution of the atomically dispersed Fe to the ORR activity.The number of atomically dispersed Fe increases as the Fe loading increased and reaching the maximum at 1.86 wt%Fe,resulting in the maximum ORR rate.Optimized Fe-N-P-C complex catalyst was used as the cathode catalyst in a homemade Zn-air battery and good performance of an energy density of 771 Wh kgZn^-1,a power density of 92.9 m W cm^-2 at 137 m A cm^-2 and an excellent durability were exhibited.     

Regulation of carbon distribution to construct high-sulfur-content cathode in lithium-sulfur batteries

Lithium-sulfur(Li-S)battery is regarded as one of the most promising next-generation energy storage systems due to the ultra-high theoretical energy density of 2600 Wh kg^-1.To address the insulation nature of sulfur,nanocarbon composition is essential to afford acceptable cycling capacity but inevitably sacrifices the actual energy density under working conditions.Therefore,rational structural design of the carbon/sulfur composite cathode is of great significance to realize satisfactory electrochemical performances with limited carbon content.Herein,the cathode carbon distribution is rationally regulated to construct high-sulfur-content and high-performance Li-S batteries.Concretely,a double-layer carbon(DLC)cathode is prepared by fabricating a surface carbon layer on the carbon/sulfur composite.The surface carbon layer not only provides more electrochemically active surfaces,but also blocks the polysulfide shuttle.Consequently,the DLC configuration with an increased sulfur content by nearly 10 wt%renders an initial areal capacity of 3.40 mAh cm^-2 and capacity retention of 83.8%during 50 cycles,which is about two times than that of the low-sulfur-content cathode.The strategy of carbon distribution regulation affords an effective pathway to construct advanced high-sulfur-content cathodes for practical high-energy-density Li-S batteries.     

Confined Ni-In intermetallic alloy nanocatalyst with excellent coking resistance for methane dry reforming

Carbon dioxide and methane are two main greenhouse gases which are contributed to serious global warming.Fortunately,dry reforming of methane(DRM),a very important reaction developed decades ago,can convert these two major greenhouse gases into value-added syngas or hydrogen.The main problem retarding its industrialization is the seriously coking formation upon the nickel-based catalysts.Herein,a series of confined indium-nickel(In-Ni)intermetallic alloy nanocatalysts(In_xNi@SiO₂)have been prepared and displayed superior coking resistance for DRM reaction.The sample containing 0.5 wt.%of In loading(In_0.5Ni@SiO₂)shows the best balance of carbon deposition resistance and DRM reactivity even after 430 h long term stability test.The boosted carbon resistance can be ascribed to the confinement of core–shell structure and to the transfer of electrons from Indium to Nickel in In-Ni intermetallic alloys due to the smaller electronegativity of In.Both the silica shell and the increase of electron cloud density on metallic Ni can weaken the ability of Ni to activate C–H bond and decrease the deep cracking process of methane.The reaction over the confined InNi intermetallic alloy nanocatalyst was conformed to the Langmuir-Hinshelwood(L-H)mechanism revealed by in situ diffuse reflectance infrared Fourier transform spectroscopy(in-situ DRIFTS).This work provides a guidance to design high performance coking resistance catalysts for methane dry reforming to efficiently utilize these two main greenhouse gases.     

Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology

An accurate and generally applicable method for estimating aqueous solubilities for a diverse set of 1297 organic compounds based on multilinear regression and artificial neural network modeling was developed. Molecular connectivity, shape, and atom-type electrotopological state (E-state) indices were used as structural parameters. The data set was divided into a training set of 884 compounds and a randomly chosen test set of 413 compounds. The structural parameters in a 30-12-1 artificial neural network included 24 atom-type E-state indices and six other topological indices, and for the test set, a predictive r2 = 0.92 and s = 0.60 were achieved. With the same parameters the statistics in the multilinear regression were r2 = 0.88 and s = 0.71, respectively.     

手性分子与手性拓扑指数

应用自制的有机化合物结构解析专家系统ESESOC, 对与28个外消旋类化合物(特殊的氨基酸)分子手性中心有关的对称性问题进行了判别, 如对称面、对称点和对称轴等. 同时用的开发的程序对这些化合物的构型进行了自动识别和标示. 在此基础上, 以手性拓扑指数为参数, 利用Fisher意义下的分析和人工神经网络法, 对这些化合物的对映体进行了高效液相色谱分离中先后淋洗出的的对映体进行分类判别. 结果表明, 人工神经网络法优于Fisher意义下的判别分析.     

Estimation of aqueous solubility of chemical compounds using E-state indices.

The molecular weight and electrotopological E-state indices were used to estimate by Artificial Neural Networks aqueous solubility for a diverse set of 1291 organic compounds. The neural network with 33-4-1 neurons provided highly predictive results with r(2) = 0.91 and RMS = 0.62. The used parameters included several combinations of E-state indices with similar properties. The calculated results were similar to those published for these data by Huuskonen (2000). However, in the current study only E-state indices were used without need of additional indices (the molecular connectivity, shape, flexibility and indicator indices) also considered in the previous study. In addition, the present neural network contained three times less hidden neurons. Smaller neural networks and use of one homogeneous set of parameters provides a more robust model for prediction of aqueous solubility of chemical compounds. Limitations of the developed method for prediction of large compounds are discussed. The developed approach is available online at http://www.lnh.unil.ch/~itetko/logp.     

饱和醇结构-保留定量相关的人工神经网络模型

以拓扑指数为结构描述符，用基于Levenberg-Marquardt优化的BP神经网络建立了醇类化合物的结构与色谱保留值的相关性模型，用于未知醇类化合物在SE－30和OV－3两根色谱柱上保留指数的同时预测，其学习速率优于文献中普通BP神经网络法，预测准确度与普通BP神经网络法接近，但优于多元线性回归法，因而是一种较好的预测有机化合物气相色谱保留指数的方法。     

Prediction of plasma protein binding of drugs using Kier–Hall valence connectivity indices and 4D-fingerprint molecular similarity analyses

Summary A 115 compound dataset for HSA binding is divided into the training set and the test set based on molecular similarity and cluster analyses. Both Kier–Hall valence connectivity indices and 4D-fingerprint similarity measures were applied to this dataset. Four different predictive schemes (SM, SA, SR, SC) were applied to the test set based on the similarity measures of each compound to the compounds in the training set. The first algorithmic scheme (SM) predicts the binding affinity of a test compound using only the most similar training set compound’s binding affinity. This scheme has relatively poor predictivity based both on Kier–Hall valence connectivity indices similarity measures and 4D-fingerprints similarity analyses. The other three algorithmic schemes (SM SR, SC), which assign a weighting coefficient to each of the top-ten most similar training set compounds, have reasonable predictivity of a test set. The algorithmic scheme which categorizes the most similar compounds into different weighted clusters predicts the test set best. The 4D-fingerprints provide 36 different individual IPE/IPE type molecular similarity measures. This study supports that some types of similarity measures are highly similar to one another for this dataset. Both the Kier–Hall valence connectivity indices similarity measures and the 4D-fingerprints have nearly same predictivity for this particular dataset.