Recent progress of advanced manganese oxide-based materials for acidic oxygen evolution reaction: Fundamentals, performance optimization,and prospects

The oxygen evolution reaction(OER) is the basis of various sustainable energy conversion and storage techniques,especially hydrogen production by water electrolysis.To realize the practical application of hydrogen energy and mass-scale hydrogen production via water electrolysis,several obstacles,such as the multi-electron transfer OER process with sluggish kinetics and overall high reaction barrier,should be overcome.Manganese oxide-based(MnOx) materials,especially MnO_(2),have emerged as promising non-noble electrocatalysts for water electro-oxidation under acidic conditions due to their wellbalanced properties between catalytic activity and stability.This review introduces the fundamental understanding of the catalytic OER process on MnOx-based materials,including the conventional adsorbate evolution mechanism(AEM) and emerging lattice oxygen oxidation mechanism(LOM).The rational screening and prediction of MnOx-based catalysts that can stably catalyze OER in acid are summarized based on Pourbaix diagram analysis and thermodynamic density functional theory(DFT) calculations.Then,the up-to-date progress of upgrading the OER catalytic performance of MnOx-based catalysts by composite construction is reviewed.Afterward,feasible strategies to improve the electrocatalytic activity and lifetime of MnOx-based catalysts are systemically discussed in terms of crystal structure control,reasonable setting of working potential and electrolyte environment,optimal selection of acid-stable conductive supports,and self-healing engineering.Finally,future scientific challenges and research directions are outlined to guide the construction of advanced MnOx-based electrocatalysts for OER in acid.

Advances of manganese-oxides-based catalysts for indoor formaldehyde removal

Formaldehyde(HCHO)has been identified as one of the most common indoor pollutions nowadays.Manganese oxides(MnO_(x))are considered to be a promising catalytic material used in indoor HCHO oxidation removal due to their high catalytic activity,low-cost,and environmentally friendly.In this paper,the progress in developing MnO_(x)-based catalysts for HCHO removal is comprehensively reviewed for exploring the mechanisms of catalytic oxidation and catalytic deactivation.The catalytic oxidation mechanisms based on three typical theory models(Mars-van-Krevelen,Eley-Rideal and Langmuir-Hinshelwood)are discussed and summarized.Furthermore,the research status of catalytic deactivation,catalysts’regeneration and integrated application of MnO_(x)-based catalysts for indoor HCHO removal are detailed in the review.Finally,the technical challenges in developing MnO_(x)-based catalysts for indoor HCHO removal are analyzed and the possible research direction is also proposed for overcoming the challenges toward practical application of such catalysts.

Synergism of preintercalated manganese ions and lattice water in vanadium oxide cathodes for high-capacity and long-life Zn-ion batteries

Aqueous Zn-ion batteries(AZIBs)are recognized as a promising energy storage system with intrinsic safety and low cost,but its applications still rely on the design of high-capacity and stable-cycling cathode materials.In this work,we present an intercalation mechanism-based cathode materials for AZIB,i.e.the vanadium oxide with pre-intercalated manganese ions and lattice water(noted as MVOH).The synergistic effect between Mn^(2+)and lattice H_(2)O not only expands the interlayer spacing,but also significantly enhances the structural stability.Systematic in-situ and ex-situ characterizations clarify the Zn^(2+)/H^(+)co–(de)intercalation mechanism of MVOH in aqueous electrolyte.The demonstrated remarkable structure stability,excellent kinetic behaviors and ion-storage mechanism together enable the MVOH to demonstrate satisfactory specific capacity of 450 mA h g^(−1)at 0.2 A g^(−1),excellent rate performance of 288.8 mA h g^(−1)at 10 A g^(−1)and long cycle life over 20,000 cycles at 5 A g^(−1).This work provides a practical cathode material,and contributes to the understanding of the ion-intercalation mechanism and structural evolution of the vanadium-based cathode for AZIBs.

Preparation of Manganese Oxide and Its Adsorption Properties

The in-situ oxidation of manganese sulfate solution with H2O_(2),sodium hypochlorite,potassium permanganate and oxygen as oxidants was investigated by means of SEM,EDS,XRD,BET and infrared analysis,and the effects of different oxidants on the morphology,phase composition,surface properties and specific surface area of manganese oxides were investigated.The experimental results show that the diameter of manganese oxide particles prepared with H_(2)O_(2)is the smallest,about 50 nm,and the specific surface area is the largest,63.8764 m^(2)/g.It has the advantages of abundant surface hydroxyl groups,no introduction of other impurities and large adsorption potential.It is most suitable to be used as an oxidant for oxidizing manganese sulfate solution to prepare manganese oxide by in-situ oxidation.Nano manganese oxide prepard by H_(2)O_(2)in-situ oxidation method is used as adsorbent to adsorb cobalt and nickel impurities in manganese sulfate.When the reaction pH is 6,the reaction time is 30min and the amount of adsorbent is 1.0 g,the adsorption rates of cobalt and nickel impurities in 100ml manganese sulfate solution are 97.59%and 97.67%,respectively.The residual amounts of cobalt and nickel meet the industrial process standard of first-class products(Co,Ni w/%≤0.005)of high-purity manganese sulfate(Hg/t4823-2015)for batteries.The study plays a guiding role in the preparation and regulation of manganese oxide,and provides a new method with high efficiency,purity and adsorbent availability for the preparation of high-purity manganese sulfate solution.

Enhanced recovery of high-purity Fe powder from iron-rich electrolytic manganese residue by slurry electrolysis

Iron-rich electrolytic manganese residue(IREMR)is an industrial waste produced during the processing of electrolytic metal manganese,and it contains certain amounts of Fe and Mn resources and other heavy metals.In this study,the slurry electrolysis technique was used to recover high-purity Fe powder from IREMR.The effects of IREMR and H2SO4 mass ratio,current density,reaction temper-ature,and electrolytic time on the leaching and current efficiencies of Fe were studied.According to the results,high-purity Fe powder can be recovered from the cathode plate,and the slurry electrolyte can be recycled.The leaching efficiency,current efficiency,and purity of Fe reached 92.58%,80.65%,and 98.72wt%,respectively,at a 1:2.5 mass ratio of H2SO4 and IREMR,reaction temperature of 60℃,electric current density of 30 mA/cm^(2),and reaction time of 8 h.In addition,vibrating sample magnetometer(VSM)analysis showed that the coercivity of electrolytic iron powder was 54.5 A/m,which reached the advanced magnetic grade of electrical pure-iron powder(DT4A coercivity standard).The slurry electrolytic method provides fundamental support for the industrial application of Fe resource recovery in IRMER.

Covalency competition induced selective bond breakage and surface reconstruction in manganese cobaltite towards enhanced electrochemical charge storage

Manganese cobaltite(MnCo_(2)_(4))is a promising electrode material because of its attractive redox chemistry and excellent charge storage capability.Our previous work demonstrated that the octahedrally-coordinated Mn are prone to react with the hydroxyl ions in alkaline electrolyte upon electrochemical cycling and separates on the surface of spinel to reconstruct into d-MnO_(2) nanosheets irreversibly,thus results in a change of the reaction mechanism with Kþion intercalation.However,the low capacity has greatly limited its practical application.Herein,we found that the tetrahedrally-coordinated Co_(2) þions were leached when MnCo_(2)_(4) was equilibrated in 1 mol L^(-1) HCl solution,leading to the formation of layered CoOOH on MnCo_(2)_(4) surface which is originated from the covalency competition induced selective breakage of the CoT–O bond in CoT–O–CoO and subsequent rearrangement of free Co_(6) octahedra.The as-formed CoOOH is stable upon cycling in alkaline electrolyte,exhibits conversion reaction mechanism with facile proton diffusion and is free of massive structural evolution,thus enables utilization of the bulk electrode material and realizes enhanced specific capacity as well as facilitated charge transfer and ion diffusion.In general,our work not only offers a feasible approach to deliberate modification of MnCo_(2)_(4)'s surface structure,but also provides an in-depth understanding of its charge storage mechanism,which enables rational design of the spinel oxides with promising charge storage properties.

Role of micro-Ca/In alloying in tailoring the microstructural characteristics and discharge performance of dilute Mg-Bi-Sn-based alloys as anodes for Mg-air batteries

The influence of micro-Ca/In alloying on the microstructural charac teristics,electrochemical behaviors and discharge properties of extruded dilute Mg-0.5Bi-0.5Sn-based(wt.%)alloys as anodes for Mg-air batteries are evaluated.The grain size and texture intensity of the Mg-Bi-Sn-based alloys are significantly decreased after the Ca/In alloying,particularly for the In-containing alloy.Note that,in addition to nanoscale Mg_(3)Bi_(2)phase,a new microscale Mg_(2)Bi_(2)Ca phase forms in the Ca-containing alloy.The electrochemical test results demonstrate that Ca/In micro-alloying can enhance the electrochemical activity.Using In to alloy the Mg-Bi-Sn-based alloy is effective in restricting the cathodic hydrogen evolution(CHE)kinetics,leading to a low self-corrosion rate,while severe CHE occurred after Ca alloying.The micro-alloying of Ca/In to Mg-Bi-Sn-based alloy strongly deteriorates the compactness of discharge products film and mitigates the"chunk effect"(CE),hence the cell voltage,anodic efficiency as well as discharge capacity are greatly improved.The In-containing alloy exhibits outstanding discharge performance under the combined effect of the modified microstructure and discharge products,thus making it a potential anode material for primary Mg-air battery.

The Influence of CO_(2) Cured Manganese Slag on the Performance andMechanical Properties of Ultra-High Performance Concrete

The presence of toxic elements in manganese slag(MSG)poses a threat to the environment due to potential pollution.Utilizing CO_(2) curing on MS offers a promising approach to immobilize toxic substances within this material,thereby mitigating their release into the natural surroundings.This study investigates the impact of CO_(2) cured MS on various rheological parameters,including slump flow,plastic viscosity(η),and yield shear stress(τ).Additionally,it assesses flexural and compressive strengths(f_(t) and f_(cu)),drying shrinkage rates(DSR),durability indicators(chloride ion migration coefficient(CMC),carbonization depth(CD)),and the leaching behavior of heavy metal elements.Microscopic examination via scanning electron microscopy(SEM)is employed to elucidate the underlying mechanisms.The results indicate that CO_(2) curing significantly enhances the slump flow of ultra-high performance concrete(UHPC)by up to 51.2%.Moreover,it reduces UHPC’sηandτby rates ranging from 0%to 52.7%and 0%to 40.2%,respectively.The DSR exhibits a linear increase corresponding to the mass ratio of CO_(2) cured MS.Furthermore,CO_(2) curing enhances both f_(t) and f_(cu) of UHPC by up to 28.7%and 17.6%,respectively.The electrical resistance is also improved,showing an increase of up to 53.7%.The relationship between mechanical strengths and electrical resistance follows a cubic relationship.The CO_(2) cured MS demonstrates a notable decrease in the CMC and CD by rates ranging from 0%to 52.6%and 0%to 26.1%,respectively.The reductions of leached chromium(Cr)and manganese(Mn)are up to 576.3%and 1312.7%,respectively.Overall,CO_(2) curing also enhances the compactness of UHPC,thereby demonstrating its potential to improve both mechanical and durability properties.

Prediction of Lubricant Physicochemical Properties Based on Gaussian Copula Data Expansion

The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of lubricants.The study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid model.The study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the lubricant.The results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small samples.The SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,respectively.Thus,this model can significantly reduce the model’s prediction error and has good prediction ability.

RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

Buckling Optimization of Curved Grid Stiffeners through the Level Set Based Density Method

Stiffened structures have great potential for improvingmechanical performance,and the study of their stability is of great interest.In this paper,the optimization of the critical buckling load factor for curved grid stiffeners is solved by using the level set based density method,where the shape and cross section(including thickness and width)of the stiffeners can be optimized simultaneously.The grid stiffeners are a combination ofmany single stiffenerswhich are projected by the corresponding level set functions.The thickness and width of each stiffener are designed to be independent variables in the projection applied to each level set function.Besides,the path of each single stiffener is described by the zero iso-contour of the level set function.All the single stiffeners are combined together by using the p-norm method to obtain the stiffener grid.The proposed method is validated by several numerical examples to optimize the critical buckling load factor.

In situ injectable hydrogel encapsulating Mn/NO-based immune nano-activator for prevention of postoperative tumor recurrence

Postoperative tumor recurrence remains a predominant cause of treatment failure. In this study, we developed an in situ injectable hydrogel, termed MPB-NO@DOX + ATRA gel, which was locally formed within the tumor resection cavity. The MPB-NO@DOX + ATRA gel was fabricated by mixing a thrombin solution, a fibrinogen solution containing all-trans retinoic acid (ATRA), and a Mn/NO-based immune nano-activator termed MPB-NO@DOX. ATRA promoted the differentiation of cancer stem cells, inhibited cancer cell migration, and affected the polarization of tumor-associated macrophages. The outer MnO2 shell disintegrated due to its reaction with glutathione and hydrogen peroxide in the cytoplasm to release Mn2+ and produce O2, resulting in the release of doxorubicin (DOX). The released DOX entered the nucleus and destroyed DNA, and the fragmented DNA cooperated with Mn2+ to activate the cGAS-STING pathway and stimulate an anti-tumor immune response. In addition, when MPB-NO@DOX was exposed to 808 nm laser irradiation, the Fe-NO bond was broken to release NO, which downregulated the expression of PD-L1 on the surface of tumor cells and reversed the immunosuppressive tumor microenvironment. In conclusion, the MPB-NO@DOX + ATRA gel exhibited excellent anti-tumor efficacy. The results of this study demonstrated the great potential of in situ injectable hydrogels in preventing postoperative tumor recurrence.

Simulation of Haloxylon Ammodendron Stand Basic Diameter Structure Based on Fuzzy Distribution Function

Based on the investigation data of 12 Haloxylon ammodendron plots in the south edge of Gurbantunggut Desert, Fuzzy distribution was introduced into the study of Haloxylon ammodendron base diameter structure fitting according to the consistency between the characteristics of Fuzzy distribution function and the distribution series of cumulative percentage of stand base diameter, and the fitting precision and effect of Fuzzy distribution function were discussed. The root mean square error RMSE and determination coefficient R2 values showed that Fuzzy-Γ1, Fuzzy-Γ2, Fuzzy-Γ3, Fuzzy-Γ4 had good fitting performance, among which Fuzzy-Γ1 had relatively high fitting precision, and its parameters were closely related to stand age and density, Fuzzy-Γ2 distribution function was the second, and Fuzzy-Γ4 distribution function had the worst fitting effect. By introducing a parameter c from the similarity of four distribution function formulas, a generalized Fuzzy distribution function Fuzzy-Γ5 is obtained. This function shows the highest fitting accuracy. Most of the values of parameter c are near 1 or 2, which shows that the diameter distribution is mainly approximate to Fuzzy-Γ1 and Fuzzy-Γ2.

Preparation of palladium-based catalyst by plasma-assisted atomic layer deposition and its applications in CO_(2) hydrogenation reduction

Supported Pd catalyst is an important noble metal material in recent years due to its high catalytic performance in CO_(2)hydrogenation.A fluidized-bed plasma assisted atomic layer deposition(FP-ALD) process is reported to fabricate Pd nanoparticle catalyst over γ-Al_(2)O_(3)or Fe_(2)O_(3)/γ-Al_(2)O_(3)support,using palladium hexafluoroacetylacetonate as the Pd precursor and H_(2)plasma as counter-reactant.Scanning transmission electron microscopy exhibits that highdensity Pd nanoparticles are uniformly dispersed over Fe_(2)O_(3)/γ-Al_(2)O_(3)support with an average diameter of 4.4 nm.The deposited Pd-Fe_(2)O_(3)/γ-Al_(2)O_(3)shows excellent catalytic performance for CO_(2)hydrogenation in a dielectric barrier discharge reactor.Under a typical condition of H_(2)to CO_(2)ratio of 4 in the feed gas,the discharge power of 19.6 W,and gas hourly space velocity of10000 h^(-1),the conversion of CO_(2)is as high as 16.3% with CH_(3)OH and CH4selectivities of 26.5%and 3.9%,respectively.

Layer by Layer Self-assembly Fiber-based Flexible Electrochemical Transistor

Poly(3,4-ethylenedioxyethiophene)-polystyrene sulfonic acid(PEDOT:PSS)/polyallyl dimethyl ammonium chloride modified reduced graphene oxide(PDDA-rGO)was layer by layer self-assembled on the cotton fiber.The surface morphology and electric property was investigated.The results confirmed the dense membrane of PEDOT:PSS and the lamellar structure of PDDA-rGO on the fibers.It has excellent electrical conductivity and mechanical properties.The fiber based electrochemical transistor(FECTs)prepared by the composite conductive fiber has a maximum output current of 8.7 mA,a transconductance peak of 10 mS,an on time of 1.37 s,an off time of 1.6 s and excellent switching stability.Most importantly,the devices by layer by layer self-assembly technology opens a path for the true integration of organic electronics with traditional textile technologies and materials,laying the foundation for their later widespread application.

The Impact of Model Based Offset Scaling Technique on the Amplitude Variation with Offset Responses from 3D Seismic Data Acquired from the Tano Basin, Offshore Ghana

Amplitudes have been found to be a function of incident angle and offset. Hence data required to test for amplitude variation with angle or offset needs to have its amplitudes for all offsets preserved and not stacked. Amplitude Variation with Offset (AVO)/Amplitude Variation with Angle (AVA) is necessary to account for information in the offset/angle parameter (mode converted S-wave and P-wave velocities). Since amplitudes are a function of the converted S- and P-waves, it is important to investigate the dependence of amplitudes on the elastic (P- and S-waves) parameters from the seismic data. By modelling these effects for different reservoir fluids via fluid substitution, various AVO geobody classes present along the well and in the entire seismic cube can be observed. AVO analysis was performed on one test well (Well_1) and 3D pre-stack angle gathers from the Tano Basin. The analysis involves creating a synthetic model to infer the effect of offset scaling techniques on amplitude responses in the Tano basin as compared to the effect of unscaled seismic data. The spectral balance process was performed to match the amplitude spectra of all angle stacks to that of the mid (26°) stack on the test lines. The process had an effect primarily on the far (34° - 40°) stacks. The frequency content of these stacks slightly increased to match that of the near and mid stacks. In offset scaling process, the root mean square (RMS) amplitude comparison between the synthetic and seismic suggests that the amplitude of the far traces should be reduced relative to the nears by up to 16%. However, the exact scaler values depend on the time window considered. This suggests that the amplitude scaling with offset delivered from seismic processing is only approximately correct and needs to be checked with well synthetics and adjusted accordingly prior to use for AVO studies. The AVO attribute volumes generated were better at resolving anomalies on spectrally balanced and offset scaled data than data delivered from conventional processing. A typical class II AVO anomaly is seen along the test well from the cross-plot analysis and AVO attribute cube which indicates an oil filled reservoir.

An Improved Bounded Conflict-Based Search for Multi-AGV Pathfinding in Automated Container Terminals

As the number of automated guided vehicles(AGVs)within automated container terminals(ACT)continues to rise,conflicts have becomemore frequent.Addressing point and edge conflicts ofAGVs,amulti-AGVconflict-free path planning model has been formulated to minimize the total path length of AGVs between shore bridges and yards.For larger terminalmaps and complex environments,the grid method is employed to model AGVs’road networks.An improved bounded conflict-based search(IBCBS)algorithmtailored to ACT is proposed,leveraging the binary tree principle to resolve conflicts and employing focal search to expand the search range.Comparative experiments involving 60 AGVs indicate a reduction in computing time by 37.397%to 64.06%while maintaining the over cost within 1.019%.Numerical experiments validate the proposed algorithm’s efficacy in enhancing efficiency and ensuring solution quality.

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

A Health State Prediction Model Based on Belief Rule Base and LSTM for Complex Systems

In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,the Density Peak Clustering(DPC)algorithmis used todetermine referential values of indicators for BRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.

Effects of Al_(2)O_(3)-SiO_(2) Raw Material Types on Properties of Anorthite Based Insulation Refractories

To optimize their Al_(2)O_(3)-SiO_(2) raw materials,anorthite based insulation refractories were prepared by the in-situ sintering process combined with the foaming method after sintering at 1350℃for 3 h,using green and pollution-free kaolin,kyanite,andalusite and sillimanite as Al_(2)O_(3)-SiO_(2) raw materials,respectively,and industrial CaCO_(3) as the CaO source.Effects of Al_(2)O_(3)-SiO_(2) raw material types on the physical properties,phase composition and microstructure were investigated.The results are as follows.All samples prepared by different Al_(2)O_(3)-SiO_(2) raw materials have hexagonal flake anorthite and a small amount of mullite and corundum.Their bulk density and thermal conductivity decrease in the order of using kaolin,andalusite,kyanite and sillimanite as the Al_(2)O_(3)-SiO_(2) raw material,but their apparent porosity increases.Moreover,in the sample with kaolin,the bonding between anorthite crystals on the pore walls is closer than that of the other samples,which is conducive to increasing the cold crushing strength.The bonding between anorthite crystals on pore walls gradually decreases in the order of using kyanite,andalusite and sillimanite as the Al_(2)O_(3)-SiO_(2) raw material,thus their cold crushing strength decreases accordingly.In comprehensive consideration,the properties of the sample from kyanite are the optimal.Its apparent porosity,thermal conductivity and cold crushing strength are 84.6%,0.141 W·m^(-1)·K^(-1) and 1.89 MPa,respectively.