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Integrating Market Sentiments for Stock Price Prediction: a Comparative Study of Bi-Lstm and Multilayer Perceptions
No full textThis study investigates the integration of sentiment analysis with machine learning models to forecast stock price movements using the Nvidia Corporation as a case study. Sentiment scores were derived from Nvidia-related financial news headlines using two sentiment analysis tools: FinBERT, a domain-specific tool, and TextBlob, a general-purpose tool. These scores were integrated into predictive frameworks based on bidirectional long short-term memory (Bi-LSTM) networks and multilayer perceptrons (MLPs) developed alongside historical stock price data. This study assesses the predictive performance over the entire observation period and across distinct market phases: bullish, stagnation, and strong bullish conditions. The findings indicate that sentiment features enhance predictive accuracy in specific contexts, particularly during stagnation phases, with TextBlob demonstrating superior performance to FinBERT in specific scenarios. In addition, Bi-LSTM models exhibit consistently superior performance in capturing temporal dependencies compared to MLPs. However, the impact of sentimentfeatures diminished during strongly directional trends, such as those observed in strong bullish markets. The combination of FinBERT and TextBlob in the same dataset allows for a dual-perspective approach to sentiment analysis, thereby providing new insights into the dynamic relationship between marketsentiment and stock price behavior. This research contributes to the existing literature on applying sentiment analysis to financial forecasting by advancing the integration of complementary sentiment tools and phase-specific evaluations
Densification of CuO-ZrO2 Nanocomposites by Flash Sintering
No full textThis study is a comprehensive investigation into CuO-doped ZrO2 nanoparticles (NPs) produced by the hydrothermal method and its conventional (CS) and flash-sintering (FS) processes. Besides this production, the effect of the differences in sintering techniques and density was investigated to prove the results. However, to the authors’ knowledge, the FS of CuO/ZrO2 nanocomposite (NC) material has yet to be studied, which is the first report on this material. The CuO/ZrO2 nanocomposite particle (NCP) pellet was sintered at 1250 oC for 1 hour using CS. The other sintering method is FS, which obtains highly dense NCs. The CuO/ZrO2 NCPs pellet was successfully produced with the lower sintering temperature (673 oC) and duration (60 seconds) by FS under a current density of 50 mA/mm2, and electric field (100 V/cm). The microstructure and density of the pellets produced from CS and FS experiments were evaluated. The SEM results showed that the CuO/ZrO2 NCPs with the FS experiment were successfully performed, and density results with 4.38 g/cm3 proved this success compared to CS pellet density (3.72 g/cm3). The FS process for CuO/ZrO2 NCPs consumes ~ 2.2 kJ (0.227 kJ/cm³), whereas CS samples require ~ 13 kJ (54 kJ/cm³), making FS approximately six times more energy-efficient. This significant reduction in energy consumption highlights FS as a promising method for future applications focused on carbon emission reduction and energy efficiency
Search for Bottom Quark Associated Production of the Standard Model Higgs Boson in Final States With Leptons in Proton-Proton Collisions at √s=13 Tev
No full textDella Ricca, Giuseppe/0000-0003-2831-6982; Lunerti, Leonardo/0000-0002-8932-0283; Akgun, Bora/0000-0001-8888-3562; Radogna, Raffaella/0000-0002-1094-5038; Giommi, Luca/0000-0003-3539-4313; Matorras, Francisco/0000-0003-4295-5668; Tiwari, Praveen Chandra/0000-0002-3667-3843; Martinez Rivero, Celso/0000-0002-3224-956X; Schieck, Jochen/0000-0002-1058-8093; Shopova, Mariana/0000-0001-6664-2493; Chatterjee, Suman/0000-0003-2660-0349; Dash, Ganapati/0000-0002-7451-4763; Hernandez Calama, Jose Maria/0000-0001-6436-7547; Tedeschi, Tommaso/0000-0002-7125-2905; Troiano, Donato/0000-0001-7236-2025; Grunewald, Martin/0000-0002-5754-0388; Venditti, Rosamaria/0000-0001-6925-8649; Shevelev, Alexey/0000-0003-4600-0228; Colaleo, Anna/0000-0002-0711-6319; Hussain, Priya Sajid/0000-0002-4825-5278; Pozniak, Krzysztof/0000-0001-5426-1423; Pedraza Morales, Maria Isabel/0000-0002-2669-4659; Dragicevic, Marko/0000-0003-1967-6783; Macchiolo, Anna/0000-0003-0199-6957; Laroze, David/0000-0002-6487-8096; Navarrete Ramos, Efren/0000-0002-5180-4020; Rossi Tisbeni, Simone/0000-0001-6776-285X; Osherson, Marc/0000-0002-9760-9976; Calderon, Dr. Alicia/0000-0002-7205-2040This Letter presents the first search for bottom quark associated production of the standard model Higgs boson, in final states with leptons. Higgs boson decays to pairs of tau leptons and pairs of leptonically decaying W bosons are considered. The search is performed using data collected from 2016 to 2018 by the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb(-1). Upper limits at the 95% confidence level are placed on the signal strength for Higgs boson production in association with bottom quarks; the observed (expected) upper limit is 3.7 (6.1) times the standard model prediction.We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.). Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 884104, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the "Excellence of Science - EOS" - be.h project n. 30820817; the Beijing Municipal Science ; Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy - EXC 2121 "Quantum Universe" -390833306, and under project number 400140256 - GRK2497; the Hungarian Academy of Sciences, the New National Excellence Program - UNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundacao para a Ciencia e a Tecnologia, grant CEECIND/01334/2018 (Portugal); MCIN/AEI/10.13039/501100011033, ERDF "a way of making Europe", and the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation, grant B05F650021 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (U.S.A.).FWF (Austria); FNRS (Belgium); FWO (Belgium); CNPq (Brazil); CAPES (Brazil); FAPERJ (Brazil); FAPERGS (Brazil); FAPESP (Brazil); BNSF (Bulgaria); MoST (China); NSFC (China); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER (Estonia); ERDF (Estonia); Academy of Finland (Finland); MEC (Finland); CEA (France); CNRS/IN2P3 (France); BMBF (Germany); DFG (Germany); HGF (Germany); NKFIH (Hungary); DAE (India); DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF (Republic of Korea); MES (Latvia); MOE (Malaysia); UM (Malaysia); BUAP (Mexico); CONACYT (Mexico); UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); FCT (Portugal); MESTD (Serbia); PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); NSTDA (Thailand); TUBITAK (Turkey); NASU (Ukraine); NSF (USA); Marie-Curie program (European Union); European Research Council (European Union); Horizon 2020 Grant (European Union) [675440, 724704, 752730, 758316, 765710, 824093, 884104]; COST Action (European Union) [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); FWO (Belgium) under the "Excellence of Science - EOS - be.h project [30820817]; Beijing Municipal Science ; Technology Commission [Z191100007219010]; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Hellenic Foundation for Research and Innovation (HFRI) (Greece) [2288]; Deutsche Forschungsgemeinschaft (DFG) [EXC 2121, 390833306, 400140256 - GRK2497]; Hungarian Academy of Sciences (Hungary); Council of Science and Industrial Research, India; Latvian Council of Science; National Science Center (Poland) [Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552]; National Priorities Research Program by Qatar National Research Fund; MCIN/AEI, ERDF "a way of making Europe"; Programa Severo Ochoa del Principado de Asturias (Spain); Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation (Thailand) [B05F650021]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (USA); BMBWF (Austria); MES (Bulgaria); CERN; CAS (China); MINCIENCIAS (Colombia); MSES (Croatia); ERC PUT (Estonia); HIP (Finland); GSRI (Greece); MSIP (Republic of Korea); LAS (Lithuania); CINVESTAV (Mexico); LNS (Mexico); SEP (Mexico); MOS (Montenegro); MES (Poland); NSC (Poland); MCIN/AEI (Spain); MST (Taipei); MHESI (Thailand); TENMAK (Turkey); STFC (United Kingdom); DOE (USA); F.R.S.-FNRS (Belgium); New National Excellence Program - UNKP (Hungary); NKFIH (Hungary) [K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA-64]; Ministry of Education and Science [2022/WK/14]; Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu (Spain) [MDM-2017-0765
Analyzing the Relationship Between the Formation of Sinkholes and Structural Deformation: a Parametric Study
No full textIn Turkey sinkhole formations have been observed in recent years, the number of which has increased over time. These sinkholes have started to cause damage to infrastructure and superstructures, especially in rural areas. In this study, considering the rapidly increasing number of sinkholes, first of all, the sinkhole formation mechanism of the region and the characteristics of the sinkhole were examined. Then, an analysis was made on the superstructure inventory of the region. According to the investigations, a numerical study was carried out considering the general characteristics of the sinkholes and the building stock. With this study, three different heights of buildings representing the building stock of the rural area were selected and thus the pressure (S) exerted by the buildings on the ground became a main parameter. In addition to these, a total of 81 finite element models with three different sinkhole widths (D) and four different sinkhole depths (L) selected at four different distances (A) from these structures were created with the finite element program. The structure and sinkhole interaction parameters obtained from the quite comprehensive data set were evaluated in the context of settlements that may occur in the structure. While creating the model, the geotechnical properties of the soil of the region were taken within the scope of the sinkhole formation mechanism. As a result of the analyses, it was observed that the depth of the sinkhole (L), the diameter of the sinkhole (D) and the distance between the sinkhole and structure (A) had a direct effect on the sinkhole-structure interaction, and the structure load had a limited effect. The results also have indicated that the sinkholestructure interaction is limited in the sinkholes formed in diameter and high distance.The authors would like to thank the Konya Technical University Sinkhole Application Research Centre and also Disaster and Emergency Management Presidency (AFAD) for their supporting.Konya Technical University Sinkhole Application Research Centre; Disaster and Emergency Management Presidency (AFAD
Cucunetcnns: Application of Novel Ensemble Deep Neural Networks for Classification of Cucumber Leaf Disease
No full textThe accurate diagnosis of plant diseases is crucial for improving agricultural productivity and ensuring global food security. This study introduces an advanced approach to cucumber leaf disease classification by integrating novel deep learning methodologies. Two custom-designed convolutional neural networks (CucuNet-CNN1 and CucuNet-CNN2) are proposed, alongside pre-trained models such as InceptionResNetV2, EfficientNetV2M, and NASNetMobile, to classify various disease types. To enhance classification performance, an ensemble model (5EnsCNNs) is developed, combining the strengths of these architectures. Additionally, a Spiking Neural Network (SNN), inspired by neuromorphic computing principles, is employed. Experimental results show that the SNN achieves a remarkable accuracy of 98.91 % in classifying six cucumber leaf diseases, surpassing the performance of individual and ensemble models. The integration of novel CNN architectures, ensemble strategies, and SNNbased methods represents a significant advancement in automated plant disease diagnosis, paving the way for more accurate and reliable agricultural diagnostics
Investigation of the Effects of Shear Reinforcement Ratio and Opening Size on the Impact Behavior of Rc Beams Produced With Geopolymer Concrete
No full textInvestigations have revealed that construction, manufacturing, and the construction sector collectively account for a significant proportion of global energy consumption and emissions. The issue of climate change has become a matter of significant concern, with the slowing down of problems caused by it and the prevention of some of them before they occur occupying a prominent position on the global agenda. Concrete remains the most prevalent building material globally. The primary component of concrete utilized in its production is cement. However, cement is a building material that requires significant energy inputs during manufacture and generates substantial carbon emissions. Consequently, research on environmentally benign alternative concrete formulations that can be produced using alternative binding agents and recycled waste materials instead of cement has witnessed a gradual surge. Research on geopolymer concrete, one of these types, has intensified increasingly in the last decade. Research investigating the behavior of reinforced concrete structural elements produced using geopolymer concrete under static and cyclic earthquake loading has gradually increased in the literature. However, a literature review reveals a paucity of studies examining the behavior of reinforced concrete (RC) members produced using geopolymer concrete under sudden dynamic loading, such as that caused by impact forces. For this reason, an experimental study was planned, and 16 RC beams produced using standard concrete and geopolymer concrete, without and with circular web openings of different sizes, with insufficient and sufficient shear strength, were tested under impact loading using a drop weight test setup. Under the effect of constant energy level impact loading applied to the specimens, the variations of acceleration, displacement, and impact loading values for time were measured, general impact behavior, failure mechanisms, and energy dissipation values were calculated and interpreted, and it was investigated how they were affected by the experimental variables examined in the study. The openings in the RC beams and the increase in the size of the openings negatively affected the performance of all beams under impact loading. In addition, the RC beams tested in the experimental study were modeled using Ls-Dyna finite element software. The values obtained from the numerical analysis were compared with the experimental results, and the extent to which successful analyses could be performed was interpreted
Kinetics Study on the Leaching of Metallic Silver With Ammonium Carbonate as an Eco-Friendly Alternative for Cyanide
No full textAbdelraheem, Mohamed Taha Osman/0000-0002-9674-2257This article describes the dissolution kinetics of metallic silver (Ag) in ammonium carbonate and hydrogen peroxide (H2O2) solution. The influences of temperature, rotation speed, H2O2 concentration, and ammonium carbonate concentration were investigated. The results indicate that ammonium carbonate concentrations between 0.025 and 0.1 M have a significant impact on the dissolution rate. The dissolution rate is positively impacted by H2O2 concentrations between 0.025 M and 0.10 M. Furthermore, there is a positive relationship between the dissolution rate of Ag and the rotation speed. Silver dissolves more readily at temperatures between 20 degrees C and 55 degrees C. However, a temperature > 40 degrees C led to the formation of a silver carbonate layer on the disc when using high concentrations of hydrogen peroxide and ammonium carbonate. The activation energy of 11.10 kJ/mol was calculated, supporting the validity of the Levich equation, which is predicated on the suggestion that mass transfer control describes the extraction rate.The authors thank the Konya Technical University for its guidance and for providing information.Konya Technical Universit
Search for a Standard Model-Like Higgs Boson in the Mass Range Between 70 and 110 Gev in the Diphoton Final State in Proton-Proton Collisions at √s=13 Tev
No full textSchieck, Jochen/0000-0002-1058-8093; Della Ricca, Giuseppe/0000-0003-2831-6982; Lunerti, Leonardo/0000-0002-8932-0283; Hall, Geoffrey/0000-0002-6299-8385; Macchiolo, Anna/0000-0003-0199-6957; Matorras, Francisco/0000-0003-4295-5668; Mikuni, Vinicius/0000-0002-1579-2421; Calderon, Dr. Alicia/0000-0002-7205-2040; Navarrete Ramos, Efren/0000-0002-5180-4020; Grunewald, Martin/0000-0002-5754-0388; Shevelev, Alexey/0000-0003-4600-0228; Giommi, Luca/0000-0003-3539-4313; Pozniak, Krzysztof/0000-0001-5426-1423; Shopova, Mariana/0000-0001-6664-2493; Venditti, Rosamaria/0000-0001-6925-8649; Tapper, Alexander/0000-0003-4543-864X; Martinez Rivero, Celso/0000-0002-3224-956X; Tiwari, Praveen Chandra/0000-0002-3667-3843; Veckalns, Viesturs/0000-0003-3676-9711; Pedraza Morales, Maria Isabel/0000-0002-2669-4659The results of a search for a standard model-like Higgs boson decaying into two photons in the mass range between 70 and 110 GeV are presented. The analysis uses the data set collected by the CMS experiment in proton-proton collisions at root s = 13 TeV corresponding to integrated luminosities of 36.3 fb(-1), 41.5 fb(-1) and 54.4 fb(-1) during the 2016, 2017, and 2018 LHC running periods, respectively. No significant excess over the background expectation is observed and 95% cofidence level upper limits are set on the product of the cross section and branching fraction for decays of an additional Higgs boson into two photons. The maximum deviation with respect to the background is seen for a mass hypothesis of 95.4 GeV with a local (global) significance of 2.9 (1.3) standard deviations. The observed upper limit ranges from 15 to 73 fb.We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: SC (Armenia), BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MOST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG, RVTT3 and MoER TK202 (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LMTLT (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MoSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA). Individuals have received support from the Indo-French Network in High Energy Physics financed by the Indo-French Center for the Promotion of Advanced Research (CEFIPRA/IFCPAR); the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Science Committee, project no. 22rl-037 (Armenia); the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the "Excellence of Science -EOS'' -be.h project n. 30820817; the Beijing Municipal Science ; Technology Commission, No. Z191100007219010 and Fundamental Research Funds for the Central Universities (China); The Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation, grant FR22985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy - EXC 2121 "Quantum Universe" -390833306, and under project number 400140256 -GRK2497; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of Sciences, the New National Excellence Program -UNKP, the NKFIH research grants K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; ICSC -National Research Center for High Performance Computing, Big Data and Quantum Computing and FAIR --Future Artficial Intelligence Research, funded by the NextGenerationEU program (Italy); the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundacao para a Ciencia e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF "a way of making Europe'', and the Programa Estatal de Fomento de la Investigacion Cientfica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation, grant B37G660013 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).FWF; FNRS; FWO (Belgium); CNPq; CAPES; FAPERJ; FAPERGS; FAPESP (Brazil); BNSF (Bulgaria); MOST; NSFC (China); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG [MoER TK202]; Academy of Finland; MEC; CEA; CNRS/IN2P3 (France); SRNSF; BMBF; DFG; HGF (Germany); NKFIH (Hungary); DAE; DST; IPM; SFI (Ireland); INFN (Italy); NRF (Republic of Korea); MES (Latvia); MOE; UM (Malaysia); BUAP; CONACYT; UASLP-FAI (Mexico); PAEC (Pakistan); FCT (Portugal); MESTD (Serbia); PCTI (Spain); Swiss Funding Agencies (Switzerland); NSTDA; TUBITAK; DOE; NSF (USA); Indo-French Center for the Promotion of Advanced Research (CEFIPRA/IFCPAR); Marie-Curie program; European Research Council; Horizon 2020 Grant [675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207]; COST Action [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Science Committee [22rl-037]; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); FWO (Belgium) under the "Excellence of Science -EOS [30820817]; Beijing Municipal Science ; Technology Commission [Z191100007219010]; Fundamental Research Funds for the Central Universities (China); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Shota Rustaveli National Science Foundation [FR22985]; Deutsche Forschungsgemeinschaft (DFG) [EXC 2121, 400140256 -GRK2497]; Hellenic Foundation for Research and Innovation (HFRI) [2288]; Hungarian Academy of Sciences [K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA-64]; Council of Science and Industrial Research, India - NextGenerationEU program (Italy); Latvian Council of Science; Ministry of Education and Science [2022/WK/14]; National Science Center [Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552]; Fundacao para a Ciencia e a Tecnologia [CEECIND/01334/2018]; National Priorities Research Program by Qatar National Research Fund; ERDF "a way of making Europe [MDM-2017-0765]; Programa Severo Ochoa del Principado de Asturias (Spain); National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation [B37G660013]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (USA
Comparison of Rare Earth Elements Recovery From Red Mud by Two Different Roasting-Leaching Methods: A Case Study of Seydisehir, Turkiye
No full textRed mud is an iron-rich by-product generated during alumina production and typically stored in waste ponds, where it poses significant environmental challenges. However, its enrichment in critical elements-particularly rare earth elements (REEs) such as Sc, Ce, and La, as well as V and Ga-makes it a promising secondary resource. Therefore, this study proposes two methods for the recovery of the REEs from red mud:(i) sulfation with sulfuric acid followed by roasting and water leaching (S-R-WL), and (ii) roasting with ammonium sulfate followed by water leaching (R-WL). In the S-R-WL process, red mud, water, and sulfuric acid were mixed in a 1:1:2 ratio. For the R-WL route, red mud and ammonium sulfate were combined in a 1:1 ratio. The effects of roasting temperature and solid-to-liquid ratio (S/L) on extraction efficiency were systematically investigated. The R-WL method achieved up to 71% Sc, 56% La, 57% Ce, and over 70% for heavy REEs (Y, Er, Yb, Ho, Dy) with extended leaching times (up to 96 h). In contrast, the S-R-WL method provided a higher Sc recovery (similar to 79%), and comparable recovery rate of Ce (similar to 57%) and La (similar to 50%) under low roasting temperatures (650 ; ring;C, 1:10 S/L). Recovery of heavy REEs also exceeded 70% after prolonged leaching. Notably, roasting above the optimum temperature led to diminished yields of REEs, while the solid-to-liquid ratio proved critical to overall extraction performance. As a result, both methods demonstrated effective REE extraction from red mud, with the R-WL method favoring heavy REEs and the S-R-WL method yielding superior Sc recovery. Also, it was determined that both sulfating agents were effective on recovery of REEs with gains over %70 under the optimum conditions: roasting temperature of 700 degrees C, roasting time 60 min, leaching temperature 25 degrees C, leaching duration 96 h, initial solid/liquid ratio is 1:20 g/ml. Given the high environmental cost of producing critical elements from primary ores, valorizing secondary waste streams such as red mud offers sustainable pathway for critical metal recovery in the aluminum industry.The study is supported by Scientific and Technological Research Council of Turkey (TUBITAK 120Y216) and Karamanoglu Mehmetbey University Scientific Research Projects (KMU-BAP-14-YL-22).Scientific and Technological Research Council of Turkey [TUBITAK 120Y216]; Karamanoglu Mehmetbey University Scientific Research Projects [KMU-BAP-14-YL-22
