Study on synergistic leaching of potassium and phosphorus from potassium feldspar and solid waste phosphogypsum via coupling reactions

To achieve the resource utilization of solid waste phosphogypsum(PG)and tackle the problem of utilizing potassium feldspar(PF),a coupled synergistic process between PG and PF is proposed in this paper.The study investigates the features of P and F in PG,and explores the decomposition of PF using hydrofluoric acid(HF)in the sulfuric acid system for K leaching and leaching of P and F in PG.The impact factors such as sulfuric acid concentration,reaction temperature,reaction time,material ratio(PG/PF),liquid–solid ratio,PF particle size,and PF calcination temperature on the leaching of P and K is systematically investigated in this paper.The results show that under optimal conditions,the leaching rate of K and P reach more than 93%and 96%,respectively.Kinetics study using shrinking core model(SCM)indicates two significant stages with internal diffusion predominantly controlling the leaching of K.The apparent activation energies of these two stages are 11.92 kJ·mol^(-1)and 11.55 kJ·mol^(-1),respectively.

Evaluating the Nexus between Housing and Energy Sectors: The Comparison of Urban, Peri-Urban and Rural Housing Areas in Zhuhai, China

China has developed several housing policy reforms and has, in recent years, progressed further on developing policies to promote green strategies and energy-saving plans. The urban housing has much benefited from such reforms and plans, while rural housing has experienced minimal change in the past few decades. As a result, this study aims to identify and explore these differences, challenges, and implications that exist between three areas of urban, peri-urban, and rural in a particular case study in China. For this study, selected residential areas in the City of Zhuhai and its peri-urban and rural areas are selected as case studies. A questionnaire survey is conducted to analyze energy and housing conditions across the more prosperous urban, the dynamic and transitory peri-urban, and the less prosperous rural areas. The case studies are analyzed as comparative examples in a hot and humid sub-tropical area of Southern China. The study compares energy and housing conditions and argues potential possibilities and challenges for the future development of housing policy reforms that are the result of current housing development patterns and green strategy situation in China.

An advanced ash fusion study on the melting behaviour of coal,oil shale and blends under gasification conditions using picture analysis and graphing method

This study investigates the potential of solid fuel blending as an effective approach to manipulate ash melting behaviour to alleviate ashrelated problems during gasification,thus improving design,operability and safety.The ash fusion characteristics of Qinghai bituminous coal together with Fushun,Xinghua and Laoheishan oil shales(and their respective blends)were quantified using a novel picture analysis and graphing method,which incorporates conventional ash fusion study,dilatometry and sintering strength test,in a CO/CO_(2)atmosphere.This imagebased characterisation method was used to monitor and quantify the complete melting behaviour of ash samples from room temperature to 1520℃.The impacts of blending on compositional changes during heating were determined experimentally via Xray diffraction and validated computationally using FactSage.Results showed that the melting point of Qinghai coal ash to be the lowest at 1116℃,but would increase up to 1208℃,1161℃and 1160℃with the addition of 30%50%of Laoheishan,Fushun,and Xinghua oil shales,respectively.The formation of highmelting anorthite and mullite structures inhibits the formation of lowmelting hercynite.However,the sintering point of Qinghai coal ash was seen to decrease from 1005℃to 855℃,834℃,and 819℃in the same blends due to the formation of lowmelting aluminosilicate.Results also showed that blending directly influences the sintering strength during the various stages of melting.The key finding from this study is that it is possible to mitigate against the severe ash slagging and fouling issue arising from high calcium and iron coals by cogasification with a high silicaalumina oil shale.Moreover,blending coals with oil shales can also modify the ash melting behaviour of fuels to create the optimal ash chemistry that meets the design specification of the gasifier,without adversely affecting thermal performance.

Kinetic and thermodynamic investigations of CO2 gasification of coal chars prepared via conventional and microwave pyrolysis

This study examined an isothermal CO2 gasification of four chars prepared via two different methods,i.e.,conventional and microwave-assisted pyrolysis,by the approach of thermogravimetric analysis.Physical,chemical,and structural behaviours of chars were examined using ultimate analysis,X-ray diffraction,and scanning electronic microscopy.Kinetic parameters were calculated by applying the shrinking unreacted core(SCM)and random pore(RPM)models.Moreover,char-CO2 gasification was further simulated by using Aspen Plus to investigate thermodynamic performances in terms of syngas composition and cold gas efficiency(CGE).The microwave-induced char has the largest C/H mass ratio and most ordered carbon structure,but the smallest gasification reactivity.Kinetic analysis indicates that the RPM is better for describing both gasification conversion and reaction rates of the studied chars,and the activation energies and pre-exponential factors varied in the range of 78.45–194.72 kJ/mol and 3.15–102,231.99 s−1,respectively.In addition,a compensation effect was noted during gasification.Finally,the microwave-derived char exhibits better thermodynamic performances than the conventional chars,with the highest CGE and CO molar concentration of 1.30%and 86.18%,respectively.Increasing the pyrolysis temperature,gasification temperature,and CO2-to-carbon molar ratio improved the CGE.

From Transitions to Transformation: A Brief Review of the Potential Impacts of COVID-19 on Boosting Digitization, Digitalization, and Systems Thinking in the Built Environment

In a short time, during the early phases of the COVID-19 pandemic outbreak, we managed to shift rapidly to use digital technologies and replace some of our daily operations with virtual modes. This shift happened so instantly and widely that it enables us to argue that the COVID-19 became a valid reason to boost some of the gradual and ongoing transitions towards faster transformations. In this study, we use gray literature to delve into arguments around the boost for digitization, digitalization, and systems thinking in the development of the built environment. This is mostly discussed from the influence of COVID-19 on some of the existing practices or the business-as-usual of the built environment sector. From technological advancement to technology use, these arguments are put forward to discuss what is likely to be the major driver of technological adoption and the shifting paradigms that are yet to be revealed. The study concludes that the current push towards new directions and development pathways are likely to be widely accepted in a shorter time. The findings of this brief study feed into existing arguments on transformative pathways due to the COVID-19 pandemic.

The Analysis of Global Warming Patterns from 1970s to 2010s

While global warming is only one part of climate change effects, it poses the highest risk to our habitats and ecologies. It is alarming that global warming has heightened in multiple locations and is intensified since the early 1970s. Since then, there are certain global warming patterns that could guide us with an overview of what mitigation and adaptation strategies should be developed in the future decades. There are certain regions affected more than another, and there are certain patterns with adverse effects on regions, sub-regions, and even continents. This study provides an insightful analysis of recent global warming patterns, those that are affecting us the most with regional climate change of different types, upsurge in frequency and intensity of natural disasters, and drastic impacts on our ecosystems around the world. By analysing the global warming patterns of these last four decades, this research study sheds light on where these patterns are coming from, how they are developing, and what are their impacts. This study is conducted through grey literature and analysis of the recorded global warming data publicly available by the NASA-GISS data centre for global temperature. This brief—but comprehensive—analysis helps us to have a better understanding of what comes next for global warming impacts, and how we should ultimately react. The study contributes to the field by discovering three key points analysed based on available data and literature on recorded global temperature, including: differences between north and south hemispheres, specific patterns due to ocean surface temperature increase, and recent impacts on particular regions. The study concludes with the importance of global scale analysis to have a more realistic understanding of the global warming patterns and their impacts on all living habitats.

The Effects of Fe₂O₃and B₂O₃on the Glass Structural, Thermal, <i>in Vitro</i>Degradation Properties of Phosphate Based Glasses

Currently, phosphate based glasses have been potential future biomaterial for medical application due to excellent cytocompatibility and fully bioresorbability. In this study, phosphate based glass system with composition of 48P2O5-12B2O3-(25-X)MgO-14CaO-1Na2O-(X)Fe2O3 (X = 6, 8, 10) and 45P2O5-(Y)B2O3-(32-Y)MgO-14CaO-1Na2O-8Fe2O3 (Y = 12, 15, 20), was prepared via a melting quenching process. The effect of replacing MgO with Fe2O3 and B2O3 on the structural, thermal, degradation properties of phosphate based glass was investigated. Fourier Transform Infrared (FTIR) spectroscopy and Raman spectroscopy analysis confirmed the polymerisation of phosphate based glass network with addition of Fe2O3, thus the processing window was observed to increase whilst the dissolution rate was reduced, attributed to the formation of Fe-O-P cross-link. As the effect on the glass structure stability was demonstrated by both B2O3 and MgO, the nonlinear variation of thermal stability and degradation behaviour was observed for glass system with substitution of MgO by B2O3. However, due to the lower dissolution rate of glass system when compared to the biocompatible phosphate based glass in preliminary study, the expected cytocompatibility could be confirmed in the downstream activities.

Integration of data-intensive,machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions

Technological advancements in recent decades have greatly transformed the field of material chemistry.Juxtaposing the accentuating energy demand with the pollution associated,urgent measures are required to ensure energy maximization,while reducing the extended experimental time cycle involved in energy production.In lieu of this,the prominence of catalysts in chemical reactions,particularly energy related reactions cannot be undermined,and thus it is critical to discover and design catalyst,towards the optimization of chemical processes and generation of sustainable energy.Most recently,artificial intelligence(AI)has been incorporated into several fields,particularly in advancing catalytic processes.The integration of intensive data set,machine learning models and robotics,provides a very powerful tool in modifying material synthesis and optimization by generating multifarious dataset amenable with machine learning techniques.The employment of robots automates the process of dataset and machine learning models integration in screening intermetallic surfaces of catalyst,with extreme accuracy and swiftness comparable to a number of human researchers.Although,the utilization of robots in catalyst discovery is still in its infancy,in this review we summarize current sway of artificial intelligence in catalyst discovery,briefly describe the application of databases,machine learning models and robots in this field,with emphasis on the consolidation of these monomeric units into a tripartite flow process.We point out current trends of machine learning and hybrid models of first principle calculations(DFT)for generating dataset,which is integrable into autonomous flow process of catalyst discovery.Also,we discuss catalyst discovery for renewable energy related reactions using this tripartite flow process with predetermined descriptors.

Charge Storage Properties of Aqueous Halide Supercapatteries with Activated Carbon and Graphene Nanoplatelets as Active Electrode Materials

Device level performance of aqueous halide supercapatteries fabricated with equal electrode mass of activated carbon or graphene nanoplatelets has been characterized.It was revealed that the surface oxygen groups in the graphitic structures of the nanoplatelets contributed toward a more enhanced charge storage capacity in bromide containing redox electrolytes.Moreover,the rate performance of the devices could be linked to the effect of the pore size of the carbons on the dynamics of the inactive alkali metal counterion of the redox halide salt.Additionally,the charge storage performance of aqueous halide supercapatteries with graphene nanoplatelets as the electrode material may be attributed to the combined effect of the porous structure on the dynamics of the non-active cations and a possible interaction of the Br^(-)/(Br_(2)+Br^(-)_(3))redox triple with the surface oxygen groups within the graphitic layer of the nanoplatelets.Generally,it has been shown that the surface groups and microstructure of electrode materials must be critically correlated with the redox electrolytes in the ongoing efforts to commercialize these devices.

The role of machine learning in carbon neutrality:Catalyst property prediction,design,and synthesis for carbon dioxide reduction

Achieving carbon neutrality is an essential part of responding to climate change caused by the deforestation and over-exploitation of natural resources that have accompanied the development of human society.The carbon dioxide reduction reaction(CO_(2)RR)is a promising strategy to capture and convert carbon dioxide(CO_(2))into value-added chemical products.However,the traditional trial-and-error method makes it expensive and time-consuming to understand the deeper mechanism behind the reaction,discover novel catalysts with superior performance and lower cost,and determine optimal support structures and electrolytes for the CO_(2)RR.Emerging machine learning(ML)techniques provide an opportunity to integrate material science and artificial intelligence,which would enable chemists to extract the implicit knowledge behind data,be guided by the insights thereby gained,and be freed from performing repetitive experiments.In this perspective article,we focus on recent ad-vancements in ML-participated CO_(2)RR applications.After a brief introduction to ML techniques and the CO_(2)RR,we first focus on ML-accelerated property prediction for potential CO_(2)RR catalysts.Then we explore ML-aided prediction of catalytic activity and selectivity.This is followed by a discussion about ML-guided catalyst and electrode design.Next,the potential application of ML-assisted experimental synthesis for the CO_(2)RR is discussed.

Possibilities-within-Constraints:Implementing the Funds of Knowledge Concept in the People’s Republic of China

This article is designed as the starting point for future research into the implementation of the funds of knowledge concept in the People’s Republic of China.Utilizing an exploratory research design,I sketch how the funds of knowledge concept could be used by teachers to empower ethnic minority and city-born migrant children disadvantaged by government policies that restrict access to educational resources and marginalize local languages.The article identifies a number of logistical and cultural constraints that complicate the implementation of the funds of knowledge concept in the Chinese context,including access to minority and city-born migrant students,guanxi,examination pressure,and cultural scripts for teaching that emphasize knowledge transmission.However,it also suggests how teachers can work within these constraints by taking advantage of curriculum reform in China,which has led to more autonomy at the local level and the creation of elective courses.Elective courses could provide the space for the deployment of strategies that draw on students’funds of knowledge.The strategies proposed include neighborhood walks,artifact collection,and pedagogies for teaching that foster collaboration between teachers and students.The article also extends the notion of funds of knowledge by suggesting how the funds of knowledge concept could be of benefit to China’s Han ethnic majority.

Scalable Synthesis of Hierarchical Antimony/Carbon Micro-/Nanohybrid Lithium/Sodium-Ion Battery Anodes Based on Dimethacrylate Monomer

A facile scalable synthesis of hierarchical Sb/C micro-/nanohybrid has been addressed in this work, which possesses the advantages of both micrometer and nanometer scale structures as lithium-ion battery anode. Difunctional methacrylate monomers are used as solvent and carbon source as well. Liquid precursor of antimony（III） n-butoxide is dissolved in the resin monomer solution, and further incorporated into the cross-linking polymer network via photo polymerization. Through calcination in argon/hydrogen atmosphere, antimony nanoparticles are in situ formed by carbothermal reduction, and homogeneously embedded in the in situ formed micrometer sized carbon matrix. The morphology, structure, crys- tallinity, spatial dispersion, composition, and electrochemical performance of the Sb/C micro-/nanohybrid are systemati- cally investigated. The cyclic and rate performance of the Sb/C micro-/nanohybrid anode have been effectively improved compared to the pure carbon anode. A reversible capacity of 362 mAh g-1 is achieved with a reasonable mass loading density after 300 cycles at 66 mA g-1, corresponding to capacity retention of 79%. With reducing mass loading density, the reversible capacity reaches 793 mAh g-1 after 100 cycles. Moreover, the electrochemical performance of Sb/C micro-/nanohybrid as sodium-ion battery anode is also investigated in this study.

Biomass-Derived Materials for Electrochemical Energy Storage and Conversion: Overview and Perspectives

Electrochemical energy storage and conversion(EESC) technology is key to the sustainable development of human society. As an abundant and renewable source, biomass has recently shown widespread applications in EESC, achieving both low environmental impact and high performances. This article provides overview and perspectives on various types of biomassderived materials, their preparation, the role in EESC and the desired features, performances and limitations, and future research efforts.

中美关系正常化的促进因素及对国际政治的影响

中美关系是当代国际关系史上最为重要的外交突破之一,是新中国成立以来中美两国关系最为重大的变化,是双方共同的战略需求和政策调整的结果,同时也是一个艰难曲折的过程。苏联因素、台湾、越南问题、国际国内局势变动对中美关系正常化的进程都产生了推动作用,中美关系的改善和正常化对世界政治格局和国际秩序产生了极为深远的影响。

Cognitive Dissonance in Chinese Luxury Consumption Market

To examine the cognitive dissonance in Chinese luxury consumption,the paper contains a review of the research and development in the field of cognitive dissonance and the distinctive motivations applicable in Chinese luxury consumption.On the basis of the review,the link between cognitive dissonance and intention to repurchase luxury goods is discussed.This research aims to investigate the impact of Chinese culture on the reflections of cognitive dissonance as well as their repurchase intentions.The study was based on quantitative research,in which the motivations and the levels of cognitive dissonance were explored in the luxury consumption domain.In the study,the effects of Chinese culture(i.e.,Mianzi,group orientation,and buying out of emotions) on luxury consumption and cognitive dissonance were also examined in order to develop a more precise research framework.This study shed light on the different levels of cognitive dissonance relative to the different dimensions of Chinese culture.

Hydrodynamic characteristics of a rectangular gas-driven inverse liquid-solid fluidized bed

The hydrodynamic characteristics of a rectangular gas-driven inverse liquid-solid fluidized bed(GDFB)using particles of different diameters and densities were investigated in detail.Rising gas bubbles cause a liquid upflow in the riser portion,enabling a liquid downflow that causes an inverse fluidization in the downer portion.Four flow regimes(fixed bed regime,initial fluidization regime,complete fluidization regime,and circulating fluidization regime)and three transition gas velocities(initial fluidization gas velocity,minimum fluidization gas velocity,and circulating fluidization gas velocity)were identified via visual observation and by monitoring the variations in the pressure drop.The axial local bed voidage(e)of the downer first decreases and then increases with the increase of the gas velocity.Both the liquid circulation velocity and the average particle velocity inside the downer increase with the increase of the gas velocity in the riser,but decrease with the particle loading.An empirical formula was proposed to successfully predict the Richardson-Zaki index“n”,and the predicted e obtained from this formula has a±5%relative error when compared with the experimental e.

Fracture behavior and self-sharpening mechanisms of polycrystalline cubic boron nitride in grinding based on cohesive element method

Unlike monocrystalline cubic boron nitride(CBN), polycrystalline CBN(PCBN) shows not only higher fracture resistance induced by tool-workpiece interaction but also better selfsharpening capability;therefore, efforts have been devoted to the study of PCBN applications in manufacturing engineering. Most of the studies, however, remain qualitative due to difficulties in experimental observations and theoretical modeling and provide limited in-depth understanding of the self-sharpening behavior/mechanism. To fill this research gap, the present study investigates the self-sharpening process of PCBN abrasives in grinding and analyzes the macro-scale fracture behavior and highly localized micro-scale crack propagation in detail. The widely employed finite element(FE) method, together with the classic Voronoi diagram and cohesive element technique,is used considering the pronounced success of FE applications in polycrystalline material modeling.Grinding trials with careful observation of the PCBN abrasive morphologies are performed to validate the proposed method. The self-sharpening details, including fracture morphology, grinding force, strain energy, and damage dissipation energy, are studied. The effects of maximum grain cut depths(MGCDs) and grinding speeds on the PCBN fracture behavior are discussed, and their optimum ranges for preferable PCBN self-sharpening performance are suggested.

Silicon lithium-ion battery anode with enhanced performance： Multiple effects of silver nanoparticles

Silicon has been regarded as one of the most promising next generation lithium-ion battery anode. How- ever, the poor cyclic stability of the Si based anode has severely limited its practical applications, which is even worse with high mass loading density （〉1 mg cm^-2 ）. A new concept has been developed to enhance the electrochemical performance of the Si nanoparticle anode. Silver nanoparticles are composited with the silicon nanoparticles in a facile way for the first time. It is found that the mechanical properties of the Si electrode have been significantly improved by the incorporation of the silver nanoparticles, leading to enhanced cyclic performance. With the Si/Ag mass ratio of 4：1, the reversible specific discharge capacity is retained as l 156 mA h g^-1 after 100 cycles at 200 mAg^-1, which is more than three times higher than that of the bare silicon （318 mA h g^-1 ）. The rate performance has been effectively improved as well due to excellent electron conductivity of the silver nanoparticles.

Development of sensor system for real-time measurement of droplet deposition of agricultural sprayers

During the chemical application process,droplet deposition on a target is an important reference indicator for evaluating the spraying technique and its performance.In order to quickly obtain deposition results in the field,this study proposed a novel system based on surface humidity sensors.The basic principle is to convert the measured physical quantity change into a capacitance change,thereby realizing the physical quantity to electrical signal conversion.An Android application for mobile terminal and the corresponding coordinator were developed,which allowed operators to control multiple sensors simultaneously through the Bluetooth.The soluble tracer detected by spectrophotometer was used to calibrate the system.The obtained results indicated a good correlation between deposition volume and voltage increment output from the newly developed system(R2 of the six nozzles with Dv0.5 ranging from 107.28μm to 396.20μm were 0.8674-0.9729),and a power regression model based on the least squares technique(R2=0.8022)was developed.In the field test,the system exhibited an optimal performance in predicting the deposition volume.Compared with the conventional method of measuring tracer concentration,the deviation was less than 10%.In addition,the system exhibited good fitting curve of the deposition distribution with droplet number results measured by the water sensitive paper method.