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Unraveling the Effect of Compositional Ratios on the Kesterite Thin-Film Solar Cells Using Machine Learning Techniques
No full textIn the Kesterite family, the Cu2ZnSn(S, Se)4 (CZTSSe) thin-film solar cells (TFSCs) have demonstrated the highest device efficiency with non-stoichiometric cation composition ratios. These composition ratios have a strong influence on the structural, optical, and electrical properties of the CZTSSe absorber layer. So, in this work, a machine learning (ML) approach is employed to evaluate the effect composition ratio on the device parameters of CZTSSe TFSCs. In particular, the bi-metallic ratios like Cu/Sn, Zn/Sn, Cu/Zn, and overall Cu/(Zn+Sn) cation composition ratio are investigated. To achieve this, different machine learning algorithms, such as decision trees (DTs) and classification and regression trees (CARTs), are used. In addition, the output performance parameters of CZTSSe TFSCs are predicted by both continuous and categorical approaches. Artificial neural networks (ANN) and XGBoost (XGB) algorithms are employed for the continuous approach. On the other hand, support vector machine and k-nearest neighbor's algorithms are also used for the categorical approach. Through the analysis, it is observed that the DT and CART algorithms provided a critical composition range well suited for the fabrication of highly efficient CZTSSe TFSCs, while the XGB and ANN showed better prediction accuracy among the tested algorithms. The present work offers valuable guidance towards the integration of the ML approach with experimental studies in the field of TFSCs
Coupling of electronic transport and defect engineering substantially enhances the thermoelectric performance of p-type TiCoSb HH alloy
No full textFurther advancements in thermoelectric technology rely on the capacity to control both electrical and thermal transport properties simultaneously. Although TiCoSb-based half-Heusler compounds are pro-mising for mid-range-temperature thermoelectric applications owing to their high Seebeck coefficient and good electrical conductivity, their high thermal conductivity has been so far the main issue to overcome. Here, we show that a combined approach of tuning the electronic properties and defect engineering en-hances the thermoelectric performance of p-type TiCoSb-based compounds. By alloying on the Co and Ti sites with Fe and Zr, respectively, an overall increase in the peak ZT value of up to similar to 90% at 823 K is achieved in Ti0.8Zr0.2Co0.85Fe0.15Sb. This enhancement is directly tied to the more pronounced metallic nature of transport upon Fe alloying combined with a significant reduction in thermal conductivity due to mass and strain field fluctuations driven by the substitution of Zr for Ti, as evidenced by the Debye-Callaway model. Further adjusting the hole concentration with aliovalent Sn doping leads to an additional increase in ZT, eventually leading to a peak value of similar to 0.54 at 823 K in Ti0.8Zr0.2Co0.85Fe0.15Sb0.96Sn0.04, which is 224% higher than in TiCo0.85Fe0.15Sb, and the highest value reported so far in Hf-free p-type TiCoSb based HH alloys.(c) 2023 Elsevier B.V. All rights reserved
Life cycle analysis on sequential recovery of copper and gold from waste printed circuit boards
No full textInformal recycling activities of waste printed circuit boards, such as pyrolysis and landfilling, cause severe environmental harm to society. Pyrolysis of resin and polymer fraction leads to the generation of toxic effluents, and landfilling causes the leaching of heavy metals into the groundwater. A sustainable and eco-friendly way to recover base and precious elements will be an economically attractive option. Current research studied the cradle-to-gate environmental impacts of the sequential recovery of copper and gold through delamination, leaching, solvent extraction, electrowinning, and cementation from waste printed circuit boards with the help of life cycle assessment. GaBi software was utilized to assess environmental impacts such as global warming, abiotic depletion (fossil), acidification potential, and human toxicity potential during the process. Inventory data was collected by conducting several experiments and from optimizing parameters for recycling and separating 4.53 g of copper and 2.25 mg of gold from 16 g of component-free waste-printed circuit boards. Results indicate that the chemical pre-treatment or delamination process for separating metal clads from the non-metallic fraction is primarily involved in the impact category. The higher impact during delamination is due to electricity consumption. The proposed study also corroborates the industrial viability of recycling valuable metals from waste printed circuit boards to minimize the environmental impacts. The outcomes of this work could be beneficial in creating the environmental guiding principle for WPCBs recycling plants
Inkjet-Printed Graphene-Modified Aluminum Current Collector for High-Voltage Lithium-Ion Battery
No full textOne of the problems plaguing the development of Li-ion batteries that can operate at high cell voltages (i.e., beyond 4.2 V; vs Li/Li+) is the corrosion of the cathode current collector, which causes electrochemical instability and impedance build-up. In this regard, here, we demonstrate the benefits of using inkjet printing technology to uniformly coat the Al current collector (AlCC) with a graphene layer to suppress the corrosion degradation of the current collector at the cathode side. The graphene ink is prepared via solvent exfoliation, and then a thin layer of graphene is coated (-,268 nm in thickness) via inkjet printing on AlCC, which is further annealed in Ar at 350 degrees C to enhance the electrical conductivity. The thickness and mass loading of the graphene film on Al foil is controlled by the number of printing cycles. The corrosion properties of the bare and printed graphene-coated AlCCs (Gr-AlCC) were evaluated by cyclic voltammetry within 1-5 V (vs Li/Li+) using a Li-ion battery electrolyte. This clearly showed the extensive corrosion degradation of the bare AlCC, which was, however, nearly completely suppressed in the presence of the printed graphene coating. Upon usage of these AlCCs for the galvanostatic cycling of homemade Li-NMC-based cathodes in Li "half" cells, the Gr-AlCC resulted in excellent cyclic stability pertaining to -,90% capacity retention after 100 cycles (@C/5) despite the usage of 4.5 V as the upper cut-off. By contrast, the bare AlCC resulted in only -,68% capacity retention under the same conditions. Furthermore, the usage of Gr-AlCC facilitated considerably superior rate capability and lower electrode impedance as compared to the bare AlCC counterpart. This highlights the importance of such passivation/protection of current collectors with conducting coatings and the utility of the scalable-cum-versatile inkjet printing technology for the same
Creep life estimation of reformer alloy using 0-projection method- A neuro fuzzy approach
No full textThis paper deals with creep life assessment and deformation behavior of 11 years service exposed primary hydrogen reformer tube made up of HP40 grade of steel using the theta-projection method. Constant load creep tests have been performed at 870 degrees C under the stress range of 50-68 MPa. The generated creep data exhibit a sig-nificant amount of scatter under identical stresses. The existence of scatter in the creep deformation data leads to substantial amount of uncertainty in the estimation of theta-parameters and hence in the assessment of creep life. An adaptive neuro fuzzy based model is proposed to study the effect of variation of these parameters on creep life prediction. The model has been compared with the experimental findings
Low cycle fatigue behavior of additive manufactured maraging steel: Influence of build orientation and heat treatment
No full textThe effect of build orientation and heat treatment (solution treatment and aging) on the low cycle fatigue (LCF) behavior of additively manufactured (AM) maraging steel, processed via powder bed fusion, using a laser beam (PBF-LB) was investigated in the present work. The build orientation has a significant influence on the LCF behavior of AM maraging steel. Un-melted defects were found to be the most detrimental to fatigue life. Defects in 90° oriented samples were more damaging than those in 45° and 0° oriented samples. Heat treatment (HT) of the AM maraging steel was found to be very effective in enhancing the fatigue life by 8–10 times due to marked strengthening by precipitation of intermetallic precipitates and reduction in size and number of AM defects. SEM fractography showed that surface and interior flaws increased susceptibility to crack propagation through the cross-section
Development of octahedral shaped Zn2TiO4 loaded Ti3C2-TiO2 ternary composite with excellent photocatalytic efficiency
No full textThe development of highly efficient and stable earth-abundant photoanode materials for photoelectrochemical water splitting is crucial for a sustainable energy economy. Being earth-abundant, 2D Ti3C2 MXene has recently emerged as a promising candidate for efficient photocatalytic performances. However, pristine Ti3C2 and its composites suffer from poor electron-hole separation and fail to prevent the spontaneous recombination process due to the poor conductivity derived from the serious agglomeration of MXene sheets during processing. Therefore, suitable heterojunction engineering of the MXene-based composites is required for their efficient photocatalytic performances. Hence, in this work, we have developed a Ti3C2-TiO2 and octahedron-shaped, nanosized Zinc titanate (Zn2TiO4) based ternary nanocomposite with optimized composition via a simple process of alkalization followed by hydrothermal. As-synthesized Ti3C2-TiO2/Zn2TiO4 (1:0.5) nanocomposite shows a 3.7-fold augmentation in photocurrent density as compared to alkali treated Ti3C2-TiO2 at a potential of 0.9 V vs Ag/AgCl resulted due to the facile charge transfer evidenced from its impedance analysis having lowest charge transfer resistance. Furthermore, the Mott-Schottky measurements reveal that the as-synthesized nanocomposites possess n-type semiconductivity and the charge carrier concentration of Ti3C2-TiO2/Zn2TiO4 (1:0.5) is almost 5.2 times higher than that of alkali-treated Ti3C2-TiO2. This work may inspire more excellent work on developing MXene-based photoanodes
Microstructure and Mechanical Properties of Tungsten Heavy Alloy Prepared Using Tungsten Metal Powder Produced from Heavy Alloy Scrap
No full textTungsten metal powder, using a hydrometallurgical route, was extracted from tungsten heavy alloy scrap that was generated during machining of penetrator cores for the manufacture of Fin Stabilised Armour Piercing Discarding Sabot (FSAPDS). The powder was subjected to extensive characterisation that included physical property evaluation and analysis of the alloy chemistry in order to assess its suitability for the preparation of tungsten heavy alloys with enhanced mechanical properties. Subsequently, a tungsten heavy alloy based on W-Ni-Co was consolidated using this powder through liquid phase sintering followed by heat treatment and swaging operations to realize long rods (similar to 500 mm). This was followed by a detailed characterisation that included microstructure and mechanical property. The mechanical properties of these rods are promising, exhibiting a good balance of tensile and impact properties, which in turn underscores the potential of these recycled powders in the production of premium quality heavy alloy long rods for stringent applications such as kinetic energy penetrators
Detection of fatigue crack initiation pre-cursor in meta-stable SS 304LN using Rayleigh surface wave harmonic generation
No full textNonlinear Rayleigh wave is allowed to propagate through cyclically damaged 304LN stainless steel for the characterization of microstructure evolved during plastic deformation. SS304LN is widely used in nuclear and petrochemical industries in piping components, heat exchangers etc. to withstand damages arising due to low cycle fatigue, thermal aging, corrosion etc. An initial monochromatic ultrasonic Rayleigh wave when interacts with degraded microstructure in material, distorts and generates higher-order harmonics. Interrupted low-cycle fatigue tests were carried out at different strain amplitudes and at each interruption, the distortion in the ultrasound wave was measured by evaluating the nonlinearity parameter which is the ratio of the amplitude of the second harmonic to the square of the amplitude of the fundamental frequency component till failure. This study shows the effectiveness of beta to characterize the microstructural evolution as well as the generation of strain-induced alpha-martensite in meta-stable 304LN stainless steel during cyclic deformation and fatigue damage accumulation
In-process monitoring of the ultraprecision machining process with convolution neural networks
No full textIn-process monitoring and quality control are the most critical aspects of the manufacturing industry, especially in ultra-precision machining (UPM) at an industrial scale. However, in-process ensuring product quality has been difficult, as any subtle change in the process influences the UPM process dynamics and the process outcome. In order to meet the increasingly soaring demand for precision components, intelligent monitoring of the machining process is essentially important and much needed. Capturing complex signal patterns through conventional signal processing for the UPM process is often challenging due to the comparably high noise levels in the industrial environment. Signals obtained during UPM are inherent transients and non-stationary, necessitating extensive and accurate features for classification. Accurate detection of anomalies may allow for quick corrective actions, reducing the degree of damage. Earlier research revealed multi-sensor analysis, which yields richer signal feature information, but the unavoidable sensor failure in conjunction with heterogeneous sensing made it challenging. In order to address the challenges, this paper investigates the feasibility of convolution neural network (CNN) for classifying abnormal and normal machining in the UPM process. The vibrational signals obtained from B & J 4533-B accelerometer during diamond turning are transformed into time-frequency-based log-spectrogram images. These images are classified using CNN, and the results show that a proposed convolutional neural network algorithm has demonstrated an accuracy of 85.92% in classifying images and thus the corresponding in-process machining status