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The purpose of this study is to investigate the thermomechanical condition on the shape memory property of Polybutylene adipate-co-terephthalate (PBAT). PBAT is a widely researched and rapidly developed biodegradable copolyester. In a tensile test, we found that the fractured PBAT samples had a heat-driven shape memory effect which piqued our interest, and it will lay a foundation for the application of PBAT in new fields (such as heat shrinkable film).

The shape memory effect of PBAT and the effect of the thermomechanical condition on its shape memory property were confirmed and systematically investigated by a thermal mechanical analyzer and tensile machine.

The results showed that the PBAT film had broad shape memory transform temperature and exhibited excellent thermomechanical stability and shape memory properties. The shape memory fixity ratio (Rf) of the PBAT films was increased with the prestrain temperature and prestrain, where the highest Rf exceeded 90%. The shape memory recovery ratio (Rr) of the PBAT films was increased with the shape memory recovery temperature and decreased with the prestrain value, and the highest Rr was almost 100%. Moreover, the PBAT films had high shape memory recovery stress which increased with the prestrain value and decreased with the prestrain temperature, and the highest shape memory recovery stress can reach 7.73 MPa.

The results showed that PBAT had a broad shape memory transform temperature, exhibited excellent thermomechanical stability and shape memory performance, especially for the sample programmed at high temperature and had a larger prestrian, which will provide a reference for the design, processing and application of PBAT-based heat shrinkable film and smart materials.

This study confirmed and systematically investigated the shape memory effect of PBAT and the effect of the thermomechanical condition on the shape memory property of PBAT.

The purpose of this study is to assess the influence of Al and Ag addition on thermal, mechanical and shape memory properties of Cu-Al-Ag alloy.

The material is synthesized in a controlled atmosphere to minimize the reaction of alloying elements with the atmosphere. Cast samples were homogenized, then subjected to hot rolling and further betatized, followed by step quenching. Eight samples were chosen for study among which first four samples varied in Al content, and the next set of four samples varied in Ag composition.

The testing yielded a result that the increase in binary alloying element decreased transformation temperature range but increased entropy and elastic energy values. It also improved the shape memory effect and mechanical properties (UTS and hardness). An increase in ternary alloying element increased transformation temperature range, entropy and elastic energy values. The shape memory effect and mechanical properties are enhanced by the increase in ternary alloying element. The study revealed that compositional variation of Al should be limited to a range of 8 to 14 Wt.% and Ag from 2 to 8 Wt.%. Microstructural and diffraction studies identified the ß’1 martensite as a desirable phase for enhancing shape memory properties.

Numerous studies have been made in exploring the transformation temperature and phase formation for similar Cu-Al-Ag shape memory alloys, but their influence on shape memory effect was not extensively studied. In the present work, the influence of Al and Ag content on shape memory characteristics is carried out to increase the design choice for engineering applications of shape memory alloy. These materials exhibit mechanical and shape memory properties within operating ranges similar to other copper-based shape memory alloys.

This study aims to examine the impacts of higher memory dependencies on a novel semiconductor material that exhibits generalized photo-piezo-thermo-elastic properties. Specifically, the research focuses on analyzing the behavior of the semiconductor under three distinct temperature models.

The study assumes a homogeneous and orthotropic piezo-semiconductor medium during photo-thermal excitation. The field equations have been devised to encompass higher order parameters, temporal delays and a specifically tailored kernel function to address the problem. The eigenmode technique is used to solve these equations and derive analytical expressions.

The research presents graphical representations of the physical field distribution across different temperatures, higher order plasma heat conduction models and time. The results reveal that the amplitude of the distribution profile is markedly affected by factors such as the memory effect, time, conductive temperature and spatial coordinates. These factors cannot be overlooked in the analysis and design of the semiconductor.

Specific cases are also discussed in detail, offering the potential to advance the creation of precise models and facilitate future simulations.

The research offers valuable information on the physical field distribution across various temperatures, allowing engineers and designers to optimize the design of semiconductor devices. Understanding the impact of memory effect, time, conductive temperature and spatial coordinates enables device performance and efficiency improvement.

This manuscript is the result of the joint efforts of the authors, who independently initiated and contributed equally to this study.

Shape memory materials are functional materials having a good number of applications due to their unique features of programmable material technology such as self-stretching, self-assembly and self-tightening. Advancements in today’s technology led to the easy fabrication of such novel materials using 3D printing techniques. When an external stimulus causes a 3D printed specimen to change shape on its own, this process is known as 4D printing. This study aims to investigate the effect of graphene nano platelet (GNPs) on the shape memory behaviour of shape memory photo polymer composites (SMPPCs) and to optimize the shape-changing response by using the Taguchi method.

SMPPCs are synthesized by blending different weight fractions (Wt.%) of flexible or soft photopolymer (FPP) resin with hard photopolymer (HPP) resin, then reinforced with GNPs at various Wt.% to the blended PP resin, and then fabricated using masked stereolithography (MSLA) apparatus. The shape memory test is conducted to assess the shape recovery time (T), shape fixity ratio (R_f), shape recovery ratio (R_r) and shape recovery rate (V_r) using Taguchi analysis by constructing an L9 orthogonal array with parameters such as Wt.% of a blend of FPP and HPP resin, Wt.% of GNPs and holding time.

SMPPCs with A3, B3 and C2 result in a faster T with 2 s, whereas SMPPCs with A1, B1 and C3 result in a longer T with 21 s. The factors A and B are ranked as the most significant in the Pareto charts that were obtained, whereas C is not significant. It can be seen from the heatmap plot that when factors A and B increase, T is decreasing and V_r is increasing. The optimum parameters for T and V_r are A3, B3 and C2 at the same time for R_f and R_r are A1, B3 and C1.

Faster shape recovery results from a higher Wt.% of FPP resin in a blend than over a true HPP resin. This is because the flexible polymer links in FPP resin activate more quickly over time. However, a minimum amount of HPP resin also needs to be maintained because it plays a role in producing higher R_f and V_r. The use of GNPs as reinforcement accelerates the T because nanographene conducts heat more quickly, releasing the temporary shape of the specimen more quickly.

The use of FPP and HPP resin blends, fabricating the 4D-printed SMPPCs specimens with MSLA technology, investigating the effect of GNPs and optimizing the process parameters using Taguchi and the work was validated using confirmation tests and regression analysis, which increases the originality and novelty.

This paper aims to better control the mechanical properties and functional properties of NiTi alloy.

NiTi alloy samples with equal atomic ratio were formed by selective laser melting (SLM). X-ray diffraction (XRD), differential scanning calorimetry (DSC), scanning electron microscopy and tensile testing methods were used to study the effects of different laser power and scanning speed on the densification behavior, phase transformation characteristics and mechanical properties of NiTi alloy.

Compared with the laser power, the variation of the keyhole effect caused by the change of scanning speed is more intense, which has a greater effect on the densification behavior of SLM NiTi alloy. The effect of the laser power on the phase transition temperature is small. The increase of scanning speed weakens the burning degree of Ni element, so phase transition temperature decreases. The results of DSC test and tensile test show that the scanning velocity can significantly change the phase transition temperature, martensite twins reorientation and stress–strain behavior of SLM NiTi alloy.

This study provides a potential method to regulate the mechanical properties and functional properties of NiTi shape memory alloy in the future and NiTi alloys formed by SLM with good elongation were obtained because the Supercellular crystal structure formed during the nonequilibrium solidification of SLM and the superfine precipitates dispersed in the alloy prevented the dislocation formation.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

Sodium alginate (Na-Alg) is a natural polysaccharide with a rich and renewable production that is widely used in the food, pharmaceutical and daily necessities industries, among other fields. The purpose of this study is to obtain a green and degradable shape memory material, calcium alginate (Ca-Alg) film was prepared and the mechanical properties, the shape memory effect of the film were investigated and confirmed.

The Ca-Alg films were prepared by Na-Alg, calcium chloride (CaCl₂) solution, and flow extension method. Dissolve sodium alginate powder, remove bubbles, pour into petri dish, dry at 60°C, add calcium chloride solution cross-linking and finally dry naturally. The effect of CaCl₂ solution concentration on the mechanical properties of the films were investigated and discussed by universal tensile tester. The shape memory behavior and degradation performance of thin films were verified and studied by the fold-deploy shape memory test and soil embedding method, respectively.

The Ca-Alg films exhibited good mechanical and shape memory properties, with a 72.2% shape memory fixity ratio and a 92.3% shape memory recovery ratio, respectively. For a period of 120 days, the film treated with a 6 wt% CaCl₂ solution degraded at a rate of approximately 53%.

Shape memory polymers (SMPs) as intelligent materials are an important research direction for the development of modern high-tech materials. On the other hand, plastic pollution is a major problem today; as a result, preparing green degradable SMPs is essential.

This study synthesized transparent and degradable shape memory Ca-Alg films using Na-Alg and CaCl₂ solution and the flow extension method.

This paper aims to study the energy ratios of plane waves on an interface of nonlocal thermoelastic halfspace (NTS) and nonlocal orthotropic piezothermoelastic half-space (NOPS).

The memory-dependent derivatives (MDDs) approach with a hyperbolic two-temperature (HTT), three-phase lag theory is used here to study how the energy ratios change at the interface with the angle of incidence.

Plane waves that travel through NTS and hit the interface as a longitudinal wave, a thermal wave, or a transversal wave send four waves into the NOPS medium and three waves back into the NTS medium. The amplitude ratios of the different waves that are reflected and transmitted are used to calculate the energy ratios of the waves. It is observed that these ratios are affected by the HTT, nonlocal and MDD parameters.

The energy ratios correspond to four distinct models; nonlocal HTT with memory, nonlocal HTT without memory, local HTT with memory and nonlocal classical-two-temperature with memory concerning the angle of incidence from 0 degree to 90 degree.

This model applies to several fields, including earthquake engineering, soil dynamics, high-energy particle physics, nuclear fusion, aeronautics and other fields where nonlocality, MDD and conductive temperature play an important role.

The authors produced the submitted document entirely on their initiative, with equal contributions from all of them.

This study aims to investigate the two-dimensional magnetohydrodynamic flow and heat transfer of a fractional Maxwell nanofluid between inclined cylinders with variable thickness. Considering the cylindrical coordinate system, the constitutive relation of the fractional viscoelastic fluid and the fractional dual-phase-lag (DPL) heat conduction model, the boundary layer governing equations are first formulated and derived.

The newly developed finite difference scheme combined with the L1 algorithm is used to numerically solve nonlinear fractional differential equations. Furthermore, the effectiveness of the algorithm is verified by a numerical example.

Based on numerical analysis, the effects of parameters on velocity and temperature are revealed. Specifically, the velocity decreases with the increase of the fractional derivative parameter α owing to memory characteristics. The temperature increase with the increase of fractional derivative parameter ß due to a decrease in thermal resistance. From a physical perspective, the phase lag of the heat flux vector and temperature gradients τ_q and τ_T exhibit opposite trends to the temperature. The ratio τ_T/τ_q plays an important role in controlling different heat conduction behaviors. Increasing the inclination angle θ, the types and volume fractions of nanoparticles Φ can increase velocity and temperature, respectively.

Fractional Maxwell nanofluid flows from a fixed-thickness pipe to an inclined variable-thickness pipe, and the fractional DPL heat conduction model based on materials is considered, which provides a basis for the safe and efficient transportation of high-viscosity and condensable fluids in industrial production.

This paper aims to study the energy ratios of plane waves on an imperfect interface of elastic half-space (EHS) and orthotropic piezothermoelastic half-space (OPHS).

The dual-phase lag (DPL) theory with memory-dependent derivatives is employed to study the variation of energy ratios at the imperfect interface.

A plane longitudinal wave (P) or transversal wave (SV) propagates through EHS and strikes at the interface. As a result, two waves are reflected, and four waves are transmitted, as shown in Figure 2. The amplitude ratios are determined by imperfect boundaries having normal stiffness and transverse stiffness. The variation of energy ratios is computed numerically for a particular model of graphite (EHS)/cadmium selenide (OPHS) and depicted graphically against the angle of incidence to consider the effect of stiffness parameters, memory and kernel functions.

The energy distribution of incident P or SV waves among various reflected and transmitted waves, as well as the interaction of waves for imperfect interface (IIF), normal stiffness interface (NSIF), transverse stiffness interface (TSIF), and welded contact interface (WCIF), are important factors to consider when studying seismic wave behavior.

The present model may be used in various disciplines, such as high-energy particle physics, earthquake engineering, nuclear fusion, aeronautics, soil dynamics and other areas where memory-dependent derivative and phase delays are significant.

In a variety of technical and geophysical scenarios, wave propagation in an elastic/piezothermoelastic medium with varying magnetic fields, initial stress, temperature, porosity, etc., gives important information regarding the presence of new and modified waves.