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Magnesium-lithium (Mg-Li) alloys are emerging lightweight materials that can find applications in the aerospace, automobile, and electronics fields. However, the widespread use of Mg-Li alloys is limited due to their low strength and processing challenges via metal casting due to the unpredictable loss of lithium by oxidation. That said, adding micron-sized high-strength hollow spherical glass microballoon (GMB) particles can improve the mechanical and damping response of Mg and its alloys, besides reducing the density. In the current study, Mg-Li syntactic foams reinforced with GMB particles were reported for the first time. Magnesium-lithium-based syntactic foams of density 1.45-1.66 g/cc were synthesized via disintegrated melt deposition technique and hot extrusion. The effect of GMB reinforcement on the mechanical, thermal, and damping characteristics of lightweight Mg-Li alloy is studied. The developed Mg-Li/GMB composites displayed improvements in the mechanical and damping response while maintaining an ~ 12% lower density than Mg. Microstructure-property correlations of the developed composites are discussed.

Stereolithography (SLA) is a popular additive manufacturing (AM) method frequently used in research and various industrial sectors. The acrylate resin used in this research is renowned for its flexibility and durability, enabling the creation of flawless 3D-printed parts with exceptional mechanical properties. This study aims to enhance the thermomechanical properties of 3D-printed hollow glass microballoon (HGM)-filled composite materials by adding minimal HGM into the acrylate resin. We investigated the material properties through uniaxial compression tests, dynamic mechanical analysis (DMA), and scanning electron microscopy (SEM). To validate the results, a numerical investigation and a machine learning (ML) approach were carried out and compared with the experimental results. Adding a small number of microballoons increases compressive strength and stiffness. The viscoelastic behavior of the samples also provides an estimate of resilience at higher temperatures, considering the addition of filler material into the resin. Our study shows that the addition of 0.04% of HGM increased compressive strength by around 99.30% compared to the neat sample, while the stiffness increased by around 31.42% compared to the neat sample at 0.05% of HGM. It can also be estimated that the suitable range of HGM addition for the resin we used exists between 0.04% and 0.05%, where the materials achieve their maximum strength and stiffness. In addition, a predictive machine learning (ML) model, namely Random Forest Regressor (RFR), shows low mean squared error (MSE), mean absolute error (MAE), and excellent R2 scores, demonstrating the goodness of the model’s performance. This modern approach can guide us to selecting a suitable filler percentage for the photopolymer resin for 3D printing and making it applicable to different engineering prospects.

This paper reports the study of hollow microballoon-filled epoxy composites also known as syntactic foams with various volume fractions of microballoons. Different mechanical and thermomechanical investigations were carried out to study the elastic and viscoelastic behavior of these foams. The density, void content, and microstructure of these materials were also studied for better characterization. In addition to the experimental testing, a representative 3D model of these syntactic foams was developed to further investigate their elastic behavior. The results indicate that changes in the volume percentage of the microballoons had a substantial impact on the elastic and viscoelastic behavior of these foams. These results will help in designing and optimizing custom-tailored syntactic foams for different engineering applications.

Polydimethylsiloxane (PDMS) is known for its exceptional mechanical properties, chemical stability, and flexibility. Recent advancements have focused on developing functional PDMS composites by integrating various functional fillers, including polymers, ceramics, and metals, for advanced applications such as electronics, medical devices, and aerospace. Consequently, there is a growing need to investigate PDMS composites to achieve higher filler loadings offering enhanced mechanical performance. This study addresses this need by utilizing the high molecular weight (MW) PDMS resin we have developed, offering its high elongation capacity of up to >6500%. We incorporated boron (B), hollow glass microballoons (HGMs), and tungsten-coated hollow glass microballoons (WHGMs) into the developed high MW PDMS. The resulting composites demonstrated excellent elastic properties and significant compression resilience (35–80%) and elastic modulus (1.28–10.15 MPa) at high filler loadings (~60 vol.%). Specifically, B/PDMS composites achieved up to 67.6 vol.% of B, HGM/PDMS composites held up to 68.6 vol.% of HGM, and WHGM/PDMS composites incorporated up to 54.0 vol.% of WHGM. These findings highlight the potential of high MW PDMS for developing high-performance PDMS composites suitable for advanced applications such as aerospace, automotive, and medical devices.

Polymer matrix syntactic foams have received a lot of interest in recent years because of their advantages, such as their lightweight and energy-absorbing properties. The purpose of this study is to produce representative elementary volume of syntactic foams with a random filling of hollow glass microspheres from neat epoxy to 0.4 v/v filling in epoxy resin. Different forms of syntactic foams have been evaluated. ANSYS software (2020, R1) was used to investigate the numerical results. The impact of the microsphere volume fraction on the elastic mechanical properties of syntactic foams was investigated. The relative modulus, density and Poisson's ratio values of foams fall as the microsphere volume fraction rises but specific modulus values of composites increases, confirming that the weight of a component can be reduced. The relative modulus, Poisson’s ratio and density of syntactic foam decreases with increasing volume fraction of microballoon, dropping to approximately 4.5, 8.9 and 30 percent respectively at 0.4 volume fraction compared to neat epoxy. Parameters for beam and plate design ensuring that with same bending and axial stiffness for beam and bending stiffness for plate, weight of a component could well be reduced. As a consequence, utilizing syntactic foams in structural applications may result in significant weight reductions.

Syntactic foams based on addition cure, room temperature vulcanizing silicone elastomer with two types of microballoons, viz. glass and ceramic, were processed and characterized. The effect of microballoon concentration (varied from 0 to 30 wt%) on the physical, mechanical, thermal, thermo-physical and ablative properties of the foams was investigated. Syntactic foams with glass microballoon (GM) possessed lower density and higher void content compared to those with ceramic microballoon (CM). The specific tensile strength and elastic modulus showed significant improvement; particularly for 20% GM filled system due to good interfacial bonding with silicone matrix. For CM syntactic foam, most of the mechanical properties showed a decline with increase in CM content because of poor interfacial properties. The trend in mechanical properties was corroborated by the microstructure evaluation through scanning electron microscopic (SEM) analyses. The thermo-physical properties such as specific heat, thermal conductivity, thermal diffusivity and co-efficient of thermal expansion showed variations with the type and content of microballoon. Thermo-gravimetric analysis (TGA) showed accelerated thermal decomposition for GM filled system; whereas, CM filled system preserved the thermal stability of silicone along with increase in char residue. Ablative evaluation showed high heat of ablation for GM filled system compared to CM based syntactic foam. The study reveals that syntactic foams based on silicone elastomer with low density and enhanced mechanical properties, suitable for critical weight-sensitive applications, can be formulated with microballoon inclusion. Selection of the appropriate type and concentration of microballoon is crucial in realizing the syntactic foams useful for the intended application.

This study aims to develop a novel technique in manufacturing nanocomposite bimodal foams containing expandable polymeric microballoons. Low density polyethylene syntactic foams were prepared via injection molding process, afterwards, a batch refoaming method was utilized to create bimodal structure. The effects of microballoon and nanoclay content and foaming time and temperature on microstructure and physical properties of foams were investigated. The results revealed that refoaming leads to a considerable decrease in density due to nucleation of microcells along with re-expansion of microballoons, as well as CO 2 diffusion in voids between the matrix and microballoon surfaces. Microballoon content has no significant effect on cell size of bimodal foams, while a great growth in cell density was observed as its content increased. Results also indicated that at low and high foaming temperature and time, melt strength and gas loss are the overcoming phenomena, respectively leading to an optimal processing temperature and time.

Cenospheres are hollow particles in fly ash, a by-product of coal burning, and are widely used as a reinforcement when developing low-density composites called syntactic foams. This study has investigated the physical, chemical, and thermal properties of cenospheres obtained from three different sources, designated as CS1, CS2, and CS3, for the development of syntactic foams. Cenospheres with particle sizes ranging from 40 to 500 μm were studied. Different particle distribution by size was observed, and the most uniform distribution of CS particles was in the case of CS2: above 74% with dimensions from 100 to 150 μm. The CS bulk had a similar density for all samples and amounted to around 0.4 g·cm−3, with a particle shell material density of 2.1 g·cm−3. Post-heat-treatment samples showed the development of a SiO2 phase in the cenospheres, which was not present in the as-received product. CS3 had the highest quantity of Si compared to the other two, showing the difference in source quality. Energy-dispersive X-ray spectrometry and a chemical analysis of the CS revealed that the main components of the studied CS were SiO2 and Al2O3. In the case of CS1 and CS2, the sum of these components was on average from 93 to 95%. In the case of CS3, the sum of SiO2 and Al2O3 did not exceed 86%, and Fe2O3 and K2O were present in appreciable quantities in CS3. Cenospheres CS1 and CS2 did not sinter during heat treatment up to 1200 °C, while sample CS3 was already subjected to sintering at 1100 °C because of the presence of a quartz phase, Fe2O3 and K2O. For the application of a metallic layer and subsequent consolidation via spark plasma sintering, CS2 can be deemed the most physically, thermally, and chemically suitable.

Purpose To evaluate the effectiveness and safety of selective ophthalmic arterial injection (SOAI) for retinoblastoma utilizing a microballoon catheter system with an M chamber. Study design Retrospective analysis. Methods and patients This study was sanctioned by theNational Cancer Center Hospital’ Independent Ethics Committee. The surgeon was a general interventional radiologist. After confirming that the distal internal carotid artery was not delineated by balloon occlusion and the ophthalmic artery was visualized using digital subtraction angiography, melphalan was manually administered. Notably, in cases presenting bilateral retinoblastoma, both eyes received treatment in a singular, low-dose procedure. Between July 2015 and December 2021, 125 patients with retinoblastoma (68 boys and 57 girls) underwent SOAI at our facility. The average age at initial treatment was 19.3 months. The study covered 250 procedures, with patients undergoing an average of 3.7 procedures. Results The success rate of the procedure was 99.2%, with a mean procedure duration of 18.3 min. Two distinct technical failures were recorded: one attributed to an internal carotid artery having a wide lumen and the other due to the ophthalmic artery remaining undetected on angiography post-balloon occlusion of the internal carotid artery. Adverse events were minimal but included bronchospasm post-procedure and severe orbital inflammation in 0.8% and 0.4% of cases, respectively. Conclusion SOAI using the microballoon catheter with the M chamber is a feasible and safe procedure for the treatment of retinoblastoma. The success rate was 99.2%. This system can be recommended as intra-arterial chemotherapy for retinoblastoma.

Composite materials like syntactic foams have complex internal microstructures that manifest high-stress concentrations due to material discontinuities occurring from hollow regions and thin walls of hollow particles or microballoons embedded in a continuous medium. Predicting the mechanical response as non-linear stress–strain curves of such heterogeneous materials from their microstructure is a challenging problem. This is true since various parameters, including the distribution and geometric properties of microballoons, dictate their response to mechanical loading. To that end, this paper presents a novel Neural Network (NN) framework called Parameterization-based Neural Network (PBNN), where we relate the composite microstructure to the non-linear response through this trained NN model. PBNN represents the stress–strain curve as a parameterized function to reduce the prediction size and predicts the function parameters for different syntactic foam microstructures. We show that compared to several common baseline models considered in this paper, the PBNN can accurately predict non-linear stress–strain responses and the corresponding parameterized functions using smaller datasets. This is enabled by extracting high-level features from the geometry data and tuning the predicted response through an auxiliary term prediction. Although built in the context of the compressive response prediction of syntactic foam composites, our NN framework applies to predict generic non-linear responses for heterogeneous materials with internal microstructures. Hence, our novel PBNN is anticipated to inspire more parameterization-related studies in different Machine Learning methods.