Development of Aczel-Alsina t-norm based linear Diophantine fuzzy aggregation operators and their applications in multi-criteria decision-making with unknown weight information.

Aczel-Alsina t-norm and t-conorm are intrinsically flexible and endow Aczel-Alsina aggregation operators with greater versatility and robustness in the aggregation process than operators rooted in other t-norms and t-conorm families. Moreover, the linear Diophantine fuzzy set (LD-FS) is one of the resilient extensions of the fuzzy sets (FSs), intuitionistic fuzzy sets (IFSs), Pythagorean fuzzy sets (PyFSs), and q-rung orthopair fuzzy sets (q-ROFSs), which has acquired prominence in decision analysis due to its exceptional efficacy in resolving ambiguous data. Keeping in view the advantages of both LD-FSs and Aczel-Alsina aggregation operators, this article aims to establish Aczel-Alsina operation rules for LD-FSs, such as Aczel-Alsina sum, Aczel-Alsina product, Aczel-Alsina scalar multiplication, and Aczel-Alsina exponentiation. Based on these operation rules, we expose the linear Diophantine fuzzy Aczel-Alsina weighted average (LDFAAWA) operator, and linear Diophantine fuzzy Aczel-Alsina weighted geometric (LDFAAWG) operator and scrutinize their distinctive characteristics and results. Additionally, based on these aggregation operators (AOs), a multi-criteria decision-making (MCDM) approach is designed and tested with a practical case study related to forecasting weather under an LD-FS setting. The developed model undergoes a comparative analysis with several prevailing approaches to demonstrate the superiority and accuracy of the proposed model. Besides, the influence of the parameter Λ on the ranking order is successfully highlighted.

Experimental Correlation of the Role of Synthesized Biochar on Thermal, Morphological, and Crystalline Properties of Coagulation Processed Poly(1,4-phenylene sulfide) Nanocomposites.

This work aimed to study the thermal and crystalline properties of poly (1,4-phenylene sulfide)@carbon char nanocomposites. Coagulation-processed nanocomposites of polyphenylene sulfide were prepared using the synthesized mesoporous nanocarbon of coconut shells as reinforcement. The mesoporous reinforcement was synthesized using a facile carbonization method. The investigation of the properties of nanocarbon was completed using SAP, XRD, and FESEM analysis. The research was further propagated via the synthesis of nanocomposites through the addition of characterized nanofiller into poly (1,4-phenylene sulfide) at five different combinations. The coagulation method was utilized for the nanocomposite formation. The obtained nanocomposite was analyzed using FTIR, TGA, DSC, and FESEM analysis. The BET surface area and average pore volume of the bio-carbon prepared from coconut shell residue were calculated to be 1517 m2/g and 2.51 nm, respectively. The addition of nanocarbon to poly (1,4-phenylene sulfide) led to an increase in thermal stability and crystallinity up to 6% loading of the filler. The lowest glass transition temperature was achieved at 6% doping of the filler into the polymer matrix. It was established that the thermal, morphological, and crystalline properties were tailored by synthesizing their nanocomposites with the mesoporous bio-nanocarbon obtained from coconut shells. There is a decline in the glass transition temperature from 126 °C to 117 °C using 6% filler. The measured crystallinity was decreased continuously, with the mixing of the filler exhibiting the incorporation of flexibility in the polymer. So, the loading of the filler into poly (1,4-phenylene sulfide) can be optimized to enhance its thermoplastic properties for surface applications.

Anti-Wear and Anti-Erosive Properties of Polymers and Their Hybrid Composites: A Critical Review of Findings and Needs.

Erosion caused by the repeated impact of particles on the surface of a substance is a common wear method resulting in the gradual and continual loss of affected objects. It is a crucial problem in several modern industries because the surfaces of various products and materials are frequently subjected to destructively erosive situations. Polymers and their hybrid materials are suitable, in powdered form, for use as coatings in several different applications. This review paper aims to provide extensive information on the erosion behaviors of thermoset and thermoplastic neat resin and their hybrid material composites. Specific attention is paid to the influence of the properties of selected materials and to impingement parameters such as the incident angle of the erodent, the impact velocity of the erodent, the nature of the erodent, and the erosion mechanism. The review further extends the information available about the erosion techniques and numerical simulation methods used for wear studies of surfaces. An investigation was carried out to allow researchers to explore the available selection of materials and methods in terms of the conditions and parameters necessary to meet current and future needs and challenges, in technologically advanced industries, relating to the protection of surfaces. During the review, which was conducted on the findings in the literature of the past fifty years, it was noted that the thermoplastic nature of composites is a key component in determining their anti-wear properties; moreover, composites with lower glass transition, higher ductility, and greater crystallinity provide better protection against erosion in advanced surface applications.

Generation of cross section for neutron induced nuclear reaction on iridium and tantalum isotope using machine learning technique.

In this work, we proposed a new approach in generating nuclear data using machine learning techniques. This paper focused on generation of nuclear cross section for neutron induced-nuclear reaction on iridium isotopes (Ir-191) and tantalum isotopes (Ta-181) target, specifically 191Ir (n,p)191Os and 181Ta (n, 2n)180Ta using random forest algorithms. The input consists of experimental datasets obtained from EXOR and simulated datasets from TALYS 1.9. We found that the regression curve generated by our model is in good agreement with the evaluated nuclear data library ENDF/B-VII.0, which is set as the benchmark. This shows a potential in building a machine learning model for generating nuclear cross section data for both well studied and understudied nuclear reaction.

Thickness Optimization of Thin-Film Tandem Organic Solar Cell.

The polymer solar cells also known as organic solar cells (OSCs) have drawn attention due to their cynosure in industrial manufacturing because of their promising properties such as low weight, highly flexible, and low-cost production. However, low η restricts the utilization of OSCs for potential applications such as low-cost energy harvesting devices. In this paper, OSCs structure based on a triple-junction tandem scheme is reported with three different absorber materials to enhance the absorption of photons which in turn improves the η, as well as its correlating performance parameters. The investigated structure gives the higher value of η = 14.33% with Jsc = 16.87 (mA/m2), Voc = 1.0 (V), and FF = 84.97% by utilizing a stack of three different absorber layers with different band energies. The proposed structure was tested under 1.5 (AM) with 1 sun (W/m2). The impact of the top, middle, and bottom subcells' thickness on η was analyzed with a terse to find the optimum thickness for three subcells to extract high η. The optimized structure was then tested with different electrode combinations, and the highest η was recorded with FTO/Ag. Moreover, the effect of upsurge temperature was also demonstrated on the investigated schematic, and it was observed that the upsurge temperature affects the photovoltaic (PV) parameters of the optimized cell and η decreases from 14.33% to 11.40% when the temperature of the device rises from 300 to 400 K.

Graphene and Iron Reinforced Polymer Composite Electromagnetic Shielding Applications: A Review.

With advancements in the automated industry, electromagnetic inferences (EMI) have been increasing over time, causing major distress among the end-users and affecting electronic appliances. The issue is not new and major work has been done, but unfortunately, the issue has not been fully eliminated. Therefore, this review intends to evaluate the previous carried-out studies on electromagnetic shielding materials with the combination of Graphene@Iron, Graphene@Polymer, Iron@Polymer and Graphene@Iron@Polymer composites in X-band frequency range and above to deal with EMI. VOSviewer was also used to perform the keyword analysis which shows how the studies are interconnected. Based on the carried-out review it was observed that the most preferable materials to deal with EMI are polymer-based composites which showed remarkable results. It is because the polymers are flexible and provide better bonding with other materials. Polydimethylsiloxane (PDMS), polyaniline (PANI), polymethyl methacrylate (PMMA) and polyvinylidene fluoride (PVDF) are effective in the X-band frequency range, and PDMS, epoxy, PVDF and PANI provide good shielding effectiveness above the X-band frequency range. However, still, many new combinations need to be examined as mostly the shielding effectiveness was achieved within the X-band frequency range where much work is required in the higher frequency range.