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This paper explores theories of motivation, including instinct theory, arousal theory, incentive theory, intrinsic theory, extrinsic theory, the ARCS model, self-determination theory, expectancy-value theory, and goal-orientation theory. Each theory is described in detail, along with its key concepts, assumptions, and implications for behavior. Intrinsic theory suggests that individuals are motivated by internal factors like enjoyment and satisfaction, while extrinsic theory suggests that external factors like rewards and social pressure drive behavior. Arousal theory says that to feel motivated, people try to keep an optimal level of activation or excitement. Incentive theory suggests that behavior is driven by the promise of rewards or the threat of punishment. The ARCS model, designed to motivate learners, incorporates elements of attention, relevance, confidence, and satisfaction. Self-determination theory proposes that individuals are motivated by their needs for autonomy, competence, and relatedness. The expectation-value theory suggests that behavior is influenced by individuals' beliefs about their ability to succeed and the value they place on the task. The goal-orientation theory suggests that individuals have different goals for engaging in a behavior. By understanding these different theories of motivation, educators, coaches, managers, and individuals may analyze what drives behavior and how to harness it to achieve their goals. In essence, a nuanced comprehension of these diverse motivation theories equips individuals across varied domains with a strategic toolkit to navigate the complex landscape of human behavior, fostering a more profound understanding of what propels actions and how to channel these insights toward the attainment of overarching goals.
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Motivación , Autonomía Personal , Humanos , Recompensa , Castigo , Conducta SocialRESUMEN
Plastics are a vital component of our daily lives in the contemporary globalization period; they are present in all facets of modern life. Because the bulk of synthetic plastics utilized in the market are non-biodegradable by nature, the issues associated with their contamination are unavoidable in an era dominated by polymers. Polyethylene terephthalate (PET), which is extensively used in industries such as automotive, packaging, textile, food, and beverages production represents a major share of these non-biodegradable polymer productions. Given its extensive application across various sectors, PET usage results in a considerable amount of post-consumer waste, majority of which require disposal after a certain period. However, the recycling of polymeric waste materials has emerged as a prominent topic in research, driven by growing environmental consciousness. Numerous studies indicate that products derived from polymeric waste can be converted into a new polymeric resource in diverse sectors, including organic coatings and regenerative medicine. This review aims to consolidate significant scientific literatures on the recycling PET waste for electrochemical device applications. It also highlights the current challenges in scaling up these processes for industrial application.
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Plásticos , Tereftalatos Polietilenos , Reciclaje , Polímeros , Embalaje de ProductosRESUMEN
This review will unveil the development of a new generation of electrochemical sensors utilizing a transition-metal-oxide-based nanocomposite with varying morphology. There has been considerable discussion on the role of transition metal oxide-based nanocomposite, including iron, nickel, copper, cobalt, zinc, platinum, manganese, conducting polymers, and their composites, in electrochemical and biosensing applications. Utilizing these materials to detect glucose and hydrogen peroxide selectively and sensitively with the correct chemical functionalization is possible. These transition metals and their oxide nanoparticles offer a potential method for electrode modification in sensors. Nanotechnology has made it feasible to develop nanostructured materials for glucose and H2O2 biosensor applications. Highly sensitive and selective biosensors with a low detection limit can detect biomolecules at nanomolar to picomolar (10-9 to 10-12 molar) concentrations to assess physiological and metabolic parameters. By mixing carbon-based materials (graphene oxide) with inorganic nanoparticles, nanocomposite biosensor devices with increased sensitivity can be made using semiconducting nanoparticles, quantum dots, organic polymers, and biomolecules.
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Flory's statistical theory (FST) has been employed to estimate the ultrasonic velocity, density, internal pressure, and several important thermophysical parameters such as the energy of vaporization, the heat of vaporization, cohesive energy density, polarity index, and solubility for eight binary mixtures of ionic liquids and water within the temperature range of 288.15 to 308.15 K. The ionic liquids chosen for this investigation are [BMim][dca], [BMim][TfO], [BMpy][TfO], [BMpyr][dca], [BMpyr][TfO], [EEPy][ESO4], [HMim][dca], and [MPy][MSO4]. The predicted values of ultrasonic velocity and density show good agreement with the data reported in the literature. It endorses the applicability of FST to these binary mixtures. A comparative analysis of the internal pressure values (Pi) determined by using FST and the standard thermodynamic approach is also presented. The results obtained for Pi using both approaches show good agreement. Besides, for the mixtures under study, the correlation between ultrasonic velocity, density, and surface tension has also been examined. The variation of thermophysical parameters with concentration and temperature changes has been utilized to explore the nature and strength of the solute-solvent interactions prevalent in these mixtures. It is pointed out that A-A-type interactions dominate over A-B-type interactions in water-rich regions of the mixtures.
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The global energy situation requires the efficient use of resources and the development of new materials and processes for meeting current energy demand. Traditional materials have been explored to large extent for use in energy saving and storage devices. Graphene, being a path-breaking discovery of the present era, has become one of the most-researched materials due to its fascinating properties, such as high tensile strength, half-integer quantum Hall effect and excellent electrical/thermal conductivity. This paper presents an in-depth review on the exploration of deploying diverse derivatives and morphologies of graphene in various energy-saving and environmentally friendly applications. Use of graphene in lubricants has resulted in improvements to anti-wear characteristics and reduced frictional losses. This comprehensive survey facilitates the researchers in selecting the appropriate graphene derivative(s) and their compatibility with various materials to fabricate high-performance composites for usage in solar cells, fuel cells, supercapacitor applications, rechargeable batteries and automotive sectors.
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This review article focuses on the aluminum-based metal matrix composites (Al-based MMCs). Studies or investigations of their mechanical and tribological properties performed by researchers worldwide in the past are presented in detail. The processing techniques and applications for Al-based MMCs are also documented here. A brief background on the composite materials, their constituents, and their classification, as well as the different matrix materials and particulates used in Al-based MMCs, can be found in this review. Then, an overview of dual-particle-size reinforced composites, heat treatment of Al alloys, and temper designations used in heat treatment are also included. In addition, the factors influencing the mechanical and wear properties of Al-based MMCs are discussed. The primary objective is that both present and future researchers and investigators will be assisted by the comprehensive knowledge compiled in this article to further explore and work towards the betterment of society in general.
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Metallic glass (MG) is a promising coating material developed to enhance the surface hardness of metallic substrates, with laser cladding having become popular to develop such coatings. MGs properties are affected by the laser cladding variables (laser power, scanning speed, spot size). Meanwhile, the substrate surface roughness significantly affects the geometry and hardness of the laser-cladded MG. In this research, Fe-based MG was laser-cladded on substrates with different surface roughness. For this purpose, the surfaces of the substrate were prepared for cladding using two methods: sandpaper polishing (SP) and sandblasting (SB), with two levels of grit size used for each method (SP150, SP240, SB40, SB100). The experiment showed that substrate surface roughness affected the geometry and hardness of laser-cladded Fe-based MG. To predict and optimize the geometry and hardness of laser-cladded Fe-based MG single tracks at different substrate surface roughness, a fuzzy logic control system (FLCS) was developed. The FLCS results indicate that it is an efficient tool to select the proper preparation technique of the substrate surface for higher clad hardness and maximum geometry to minimize the number of cladding tracks for full surface cladding.
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In this study, the hardness and surface roughness of selective laser-melted parts have been evaluated by considering a wide variety of input parameters. The Invar-36 has been considered a workpiece material that is mainly used in the aerospace industry for making parts as well as widely used in bimetallic thermostats. It is the mechanical properties and metallurgical properties of parts that drive the final product's quality in today's competitive marketplace. The study aims to examine how laser power, scanning speed, and orientation influence fabricated specimens. Using ANOVA, the established models were tested and the parameters were evaluated for their significance in predicting response. In the next step, the fuzzy-based JAYA algorithm has been implemented to determine which parameter is optimal in the proposed study. In addition, the optimal parametric combination obtained by the JAYA algorithm was compared with the optimal parametric combination obtained by TLBO and genetic algorithm (GA) to establish the effectiveness of the JAYA algorithm. Based on the results, an orientation of 90°, 136 KW of laser power, and 650 mm/s scanning speed were found to be the best combination of process parameters for generating the desired hardness and roughness for the Invar-36 material.
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Environmental and human-friendly welding is the need of the hour. In this context, this study explores the application of the regulated metal deposition (RMD) technique for ASTM A387-Gr.11-Cl.2 steel plates. To examine the effect of metal-cored filler wire (MCFW), MEGAFIL 237 M was employed during regulated metal deposition (RMD) welding of 6 mm thick ASTM A387-Gr.11-Cl.2 steel plates. The welding was carried out at an optimized current (A) of 100 A, voltage (V) of 13 V, and gas flow rate (GFR) of 21 L/min. Thereafter, the as-welded plates were examined for morphological changes using optical microscopy. Additionally, the micro-hardness of the as-welded plates was measured to make corroboration with the obtained surface morphologies. In addition to this, the as-welded plates were subjected to heat treatment followed by surface morphology and micro-hardness examination. A comparison was made between the as-welded and heat-treated plates for their obtained surface morphologies and microhardness values. During this, it was observed that the weld zone of as-welded plates has a dendritic surface morphology which is very common in fusion-based welding. Similarly, the weld zone of heat-treated plates has a finer and erratic arrangement of martensite. Moreover, the obtained surface morphologies in the weld zone of as-welded and heat-treated plates have been justified by their respective hardness values of 1588.6 HV and 227.3 HV.
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Significant advances in the field of composite structures continue to be made on a variety of fronts, including theoretical studies based on advances in structural theory kinematics and computer models of structural elements employing advanced theories and unique formulations. Plate vibration is a persistently interesting subject owing to its wider usage as a structural component in the industry. The current study was carried out using the Co continuous eight-noded quadrilateral shear-flexible element having five nodal degrees of freedom, which is ground on first-order shear deformation theory (FSDT). For small strain and sufficiently large deformation, the geometric nonlinearity is integrated using the Von Kármán assumption. The governing equations in the time domain are solved employing the modified shooting technique along with an arc-length and pseudo-arc-length continuation strategy. This work explored the effect of fiber angle on the steady-state nonlinear forced vibration response. To explain hardening nonlinearity, the strain and stress fluctuation throughout the thickness for a rectangular laminated composite plate is determined. The cyclic fluctuation of the steady-state nonlinear normal stress during a time period at the centre of the top/bottom surfaces is also provided at the forcing frequency ratio of peak amplitude in a nonlinear response. Because of the variation in restoring forces, the frequency spectra for all fiber angle orientations show significantly enhanced harmonic participation in addition to the fundamental harmonic.
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This paper shows the novel approach of Taguchi-Based Grey Relational Analysis of Ti6Al4V Machining parameter. Ti6Al4V metal matrix composite has been fabricated using the powder metallurgy route. Here, all the components of TI6Al4V machining forces, including longitudinal force (Fx), radial force (Fy), tangential force (Fz), surface roughness and material removal rate (MRR) are measured during the facing operation. The effect of three process parameters, cutting speed, tool feed and cutting depth, is being studied on the matching responses. Orthogonal design of experiment (Taguchi L9) has been adopted to execute the process parameters in each level. To validate the process output parameters, the Grey Relational Analysis (GRA) optimization approach was applied. The percentage contribution of machining parameters to the parameter of response performance was interpreted through variance analysis (ANOVA). Through the GRA process, the emphasis was on the fact that for TI6Al4V metal matrix composite among all machining parameters, tool feed serves as the highest contribution to the output responses accompanied by the cutting depth with the cutting speed in addition. From optimal testing, it is found that for minimization of machining forces, maximization of MRR and minimization of Ra, the best combinations of input parameters are the 2nd stage of cutting speed (175 m/min), the 3rd stage of feed (0.25 mm/edge) as well as the 2nd stage of cutting depth (1.2 mm). It is also found that hardness of Ti6Al4V MMC is 59.4 HRA and composition of that material remain the same after milling operation.