This study examines the thermal and thermomechanical properties of polybutylene terephthalate/polyethylene terephthalate (PBT/PET) nanocomposites reinforced with graphene nanoplatelets (GNP) and graphene oxide (GO), and compatibilized with maleic anhydride grafted polypropylene (PP-g-MA) and maleic anhydride grafted styrene-ethylene/butylene-styrene (SEBS-g-MA). Differential scanning calorimetry (DSC) showed that PBT/PET blends were miscible in the amorphous region (single Tg) and separated in the crystalline region (double Tm). DSC results indicated increased crystallinity (Xc) in the nanocomposites due to the nucleating effect of the nanofillers, which were dispersed in both PBT and PET phases. However, the addition of compatibilizers slightly reduced the total Xc of the nanocomposites. This reduction is attributed to the interactions between compatibilizers, nanofillers, and the matrix, which restricted chain movement hence, decreasing the crystallinity. Significant enhancements in heat deflection temperature (HDT) were observed relative to the blend, regardless of the compatibilizers used. The maximum HDT was achieved at 1.5 phr for both GNP and GO nanocomposites, with an increase of 41

Developing a sustainable and biodegradable biocomposite for 3D printing necessitates iterative experimentation to achieve the desired composition and properties. Combining polylactic acid (PLA), epoxidized palm oil (EPO), and lignin offers a promising formulation for a 3D biocomposite filament with diverse potential applications. To optimize its performance specifically for 3D printing applications, the use of machine learning techniques can expedite the process of property optimization. Employing three distinct machine learning models, namely random forest (RF), support vector regression (SVR), and artificial neural network (ANN), facilitates a comparative analysis to determine the most effective approach for predicting the tensile strength values across different biocomposite compositions. Through model training and evaluation, the comparison reveals that both RF and SVR demonstrate superior accuracy compared to ANN. Notably, RF exhibits exceptional consistency, boasting an average R2 score of 0.9777 and an average mean squared error of 1.5475. SVR follows closely with an average R2 score of 0.9777 and an average mean squared error of 7.7751, while ANN lags behind with an average R2 score of 0.6551 and an average mean squared error of 117.5218. Assessing the performance of these machine learning models underscores their potential applicability in enhancing the production of biocomposite filaments for 3D printing, thereby facilitating the refinement of biocomposite properties.

The increasing demand for sustainable and degradable polymeric materials derived from renewable resources has led to the widespread adoption of polylactic acid (PLA) due to its biodegradability, renewable sourcing, and notable mechanical strength. Despite these advantages, PLA's inherent brittleness limits its use in extrusion and 3D printing applications. This study investigates the incorporation of bio-based thermoplastic polyurethane (bTPU) to improve the flexibility and toughness of PLA. PLA/bTPU blends were produced with varying bTPU content (10, 20, 30, 40 wt

Polymer encapsulation is commonly adopted in drug delivery systems to form encapsulation that can assist in delivering active compounds to the targeted area. Acalypha indica (AI) crude extract was obtained from AI plants through ultrasound-assisted extraction. It is naturally unstable in the external environment and, thus, needs to be encapsulated to protect against volatility. Herein, this study emphasized the development of the encapsulations of AI extracts using a chitosan-polycaprolactone (PCL) blend by emulsion-solvent evaporation and freeze-dried methods. Four parameters for Al encapsulation were studied by fixing one parameter at a time. The percentage of encapsulation efficiency (EE%) was recorded as a response for each parameter. The study proceeded with central composite design (CCD) as the response surface methodology (RSM) optimization tool to study the interactions between the factors. Central points were taken from the preliminary data obtained in one-parameter experiments. The validation was carried out with two data of the highest and lowest EE% suggested by CCD. The highest EE% recorded was 98.70%, and the lowest EE% was 87.80%. The results showed a difference between predicted and experimental values at a percentage lower than 7.5%. Fourier Transform Infrared Spectroscopy (FTIR), scanning electron microscopy (SEM), particle size analyzer, and zeta potential were used to analyze the properties of selected microencapsulated samples. Overall, the encapsulation of AI extracts was successful and has the potential to be used in drug delivery.

The allure of 4D printing and machine learning (ML) for various applications is unquestionable, and researchers are striving hard to improve their performance. In this work, machine learning has been applied to predict the tensile strength of the 4D printed materials. The study investigated the reinforcement of polylactic acid (PLA) filament with lignin from oil palm empty fruit bunches (OPEFB) in the presence of epoxidized palm oil (EPO) as 4D printable filament. The alkaline extraction method was carried out used sodium hydroxide (NaOH), followed by precipitation with mineral acids utilizing one-factor-at-a-time (OFAT). Thereafter, the tensile strength of the 4D printed material was evaluated by tensile testing machine followed by machine learning prediction in which convolutional neural network (CNN) was adopted. The morphology of the 4D printed materials was determined by scanning electron microscope (SEM). The SEM micrograph of the tensile test of biocomposites revealed layer-by-layer formation of the filaments on the printed unfilled PLA biocomposite indicating lower inter-filament bonding. In the first trial, the actual result of the experiment was evaluated to be 24.44 MPa while the CNN prediction was 25.53 MPa. In the second attempt, the actual result of the experiment was 31.61 MPa whereas the prediction from CNN was 27.55 MPa. The coefficient of determination value obtained from CNN prediction is 0.12662. The current study indicates that machine learning is an important tool to optimize and/or predict the properties of 4D printing materials.

Gold nanoparticles (AuNPs) are arguably the most promising in terms of application among the noble metal nanoparticles. As such, researchers have dedicated a considerable amount of energy towards the synthesis of novel gold nanoparticles with different morphology and size, which in turn affect its potential application. This study synthesized novel spherical-shaped AuNPs via green reduction of gold ion in an aqueous solution of HAuCl4.3H2O in the presence of trimethyl chitosan as a stabilizing agent and pressurized H2 gas as a reducing agent on an ice water. The size, morphology, chemical composition, chemical environment and optical activities were examined by scanning electron microscope (SEM), high-resolution transmission electron microscope (HR-TEM), x-ray photoelectron spectroscopy (XPS), and ultraviolet–visible spectroscopy (Uv–Visible), respectively. The average particle size of AuNPs is 37 nm while HR-TEM revealed a unique spherical morphology. The Tauc's plot calculation revealed a band gap of 5.0 eV. This unique morphology and optical local field enhancement in plasmonic nanocomposites could be suitable alternative for conducting devices and photocatalysis.

This systematic review explores the integration of 4D/3D printing technologies with machine learning, shaping a new era of manufacturing innovation. The analysis covers a wide range of research papers, articles, and patents, presenting a multidimensional perspective on the advancements in additive manufacturing. The review underscores machine learning's pivotal role in optimizing 4D/3D printing, addressing aspects like design customization, material selection, process control, and quality assurance. The examination reveals novel techniques enabling the fabrication of intelligent, self-adaptive structures capable of transformation over time. Additionally, the review investigates the use of predictive algorithms to enhance efficiency, reliability, and sustainability in 4D/3D printing processes. Applications span aerospace, healthcare, architecture, and consumer goods, showcasing the potential to create intricate, personalized, and once-unattainable functional products. The synergy between machine learning and 4D/3D printing is poised to unlock new manufacturing horizons, enabling rapid responses to market demands and sustainability challenges. In summary, this review provides a comprehensive overview of the current state of 4D/3D printing optimization through machine learning, highlighting the transformative potential of this interdisciplinary fusion and offering a roadmap for future research and development. It aims to inspire innovators, researchers, and industries to harness this powerful combination for accelerated evolution in manufacturing processes into the 21st century and beyond.

Hydrogel, a water-swollen polymeric network, has emerged as an appealing protagonist in the realm of 3D/4D printing technology. This review offers a glimpse into the promising world of hydrogel-based additive manufacturing and its profound impact on the technological landscape.In an era where customization, sustainability, and adaptability are paramount, hydrogel-infused 3D/4D printing offers a tantalizing glimpse into the future of design, manufacturing, and healthcare. This review explores the novel and exciting area of 3D and 4D printing, with a specific focus on the application of hydrogels in this revolutionary manufacturing process. Hydrogels, as intelligent and stimuli-responsive materials, hold great promise in the field of 3D and 4D printing due to their ability to change their shape and properties over time in response to external stimuli. This review provides an in-depth analysis of the current state of research in addictive manufacturing, emerging application of hydrogel in 3D and 4D printing, current challenges, and opportunities in utilizing hydrogels for 4D printing applications.

3D printing is one of the growing technologies in the entry era of the industrial revolution 4.0. Fused Deposition Modelling (FDM), one of the 3D printing methods, has advantages in the manufacturing process where various product shapes can be made. One of the advantages of FDM lies in the extruder used. Various types of extruders can be used and installed on FDM machines. Findings from a review of five types of extruders found that some models have the ability to extrude specific types of material. Each extruder has advantages and specializations, which can affect the printing result. Therefore, this paper reviews the types of extruders for FDM and their capabilities so that the selection of the type of extruder to be used can be made accurately.

The effects of various filler contents on the thermal, dynamic mechanical, mechanical, as well as flammability properties of halloysite nanotubes (HNTs) filler and polyamide 11 (PA 11) matrixes are investigated in this research. The nanocomposites were made out of 100 phr of PA 11 and three distinct HNTs loadings of 2, 4, and 6 phr each. PA 11 nanocomposites without HNTs filler was used as the reference sample. To melt-compound the nanocomposites, a twin-screw extruder was used, and the specimen for testing was then injected using an injection mold. SEM, TGA, DSC, FTIR, DMA, tensile, flexural, impact, and UL-94 flammability tests were conducted on the nanocomposites. Incorporation of 4 phr HNTs into the nanocomposites resulted in the highest tensile and flexural strength. Maximum improvement in the DMA, Young’s and flexural modulus was achieved at 6 phr HNTs content. The elongation at break and TGA resulted the highest increase at 2 phr HNTs content. However, the impact strength decreased with increasing HNTs content. Scanning electron microscopy revealed the ductility of the nanocomposites with increased HNTs content up to 4 phr. The DSC showed a steady increase in melting temperature (Tm) as HNTs content increased up to 4 phr, while the crystallization temperature (Tc) remained unchanged. TGA of PA 11/HNTs nanocomposites showed high thermal stability at 2 phr HNTs content. However, on further addition of HNTs up to 6 phr, thermal stability of the nanocomposites decreased due to the excess amount of HNTs. All the nanocomposites passed the horizontal and vertical UL-94 test with HB and V-2 grade. PA 11/4HNTs nanocomposite has the highest tensile strength, flexural strength compared to other PA 11/HNTs nanocomposites. PA 11/4HNTs nanocomposite can be suggested as an optimum formulation with balanced mechanical properties in terms of toughness.

Banana fiber (BF) is a renewable resource which can be a good reinforcer to polylactic acid, enhancing the polymers stiffness and accelerating biodegradability rates. However, due to the difference in nature between banana fibers (hydrophilic) and polylactic acid (hydrophobic), a good adhesion between the fiber and the matrix cannot be achieved. To tackle the problem, the fibers were chemically treated using sodium hydroxide under three factors (temperature, concentration, and treatment time). The factors interactions were given by response surface methodology and the statistical interactions were observed by ANOVA. Alkaline effect on fiber's morphology, composition, mechanical, and morphological properties of composites produced were observed. Young's modulus values have increased substantially compared to untreated fiber's composites, with a slight increase in the tensile strength values. However, the elongation at break values have slightly dropped with the fiber's alkaline treatment. Scanning electron microscope observations had shown a successful removal of impurities (e.g. hemicellulose, wax) and between the fiber's strands, and an improvement of surface structure. Fourier-transform infrared spectroscopy analysis showed the successful removal of impurities, and interaction between PLA and banana fibers.

In this study, polyamide 11 (PA 11) and halloysite nanotubes (HNTs) with varying magnesium hydroxide (MH) contents were prepared using a twin-screw extruder and injection moulding process. The mechanical properties of nanocomposites were investigated. The nanocomposites are made up of 100 phr of PA11 and 4 phr of HNTs, with three different MH loadings of 10, 20 and 30 phr. Tensile and flexural strength showed slightly increase while Young's and flexural modulus continuously increased with addition of MH. Meanwhile, the impact strength and elongation at break decreases.

Since the first discovery of Ti3C2 in the year 2011, the family of 2D transition metal carbides carbonitrides, and nitrides (commonly referred to as MXenes) has grown significantly. The materials described so far all have surface terminations like hydroxyl, oxygen, or fluorine that give their surfaces a hydrophilic quality. Due to MXenes' adaptable chemistry, they can be tailored for a variety of purposes, such as energy storage, electromagnetic interference shielding, reinforcement for composites, water purification, gas- and biosensors, lubrication, and photo-, electro-, and chemical catalysis. As such, this paper provides a critical overview of the synthesis and properties of MXene, the various routes through which MXene can be chemically and physically modified, and the different composites of MXenes, among others. Also, we robustly discussed the application of MXene composites in rechargeable batteries and assessed the effectiveness of MXene composites. Finally, an outlook and current challenges confronting the synthesis and application of MXene composites in rechargeable batteries were highlighted for future exploration.

The effect of Supercritical Carbon Dioxide (SCCO2) pressure on foamed PolyLactic Acid (PLA) were studied in this work. Foamed PLA biocomposite thin film was created by combining PLA with Durian Skin Fibre (DSF) and Cinnamon Essential Oil (CEO) before being treated with supercritical carbon dioxide (SCCO2). The morphological structure was studied to see the effect of pressure on the cell growth of the foamed polymer, later the correlation of morphology structure and tensile strength showing that the cell growth did hugely effect the tensile strength of the PLA biocomposite thin film.

Global warming is caused by excessive CO2 production, and reducing CO2 emissions is a viable way to counteract this. It has been extensively studied how light-driven processes, particularly photocatalytic systems, can trans-form solar energy into chemical energy. In the present review exercise, the mechanism of CO2 reduction is described using calculations based on density functional theory (DFT), and comparisons are also made with regard to typical light-driven devices. Additionally, the traits of potential materials-including metal-organic frameworks (MOFs), metal complexes, metal oxide, Z-scheme (metal complexes/semiconductors, two semi-conductors, dye-sensitized semiconductors), improved S-scheme and organic photocatalyst etc.-are described in depth to show how these traits affect the CO2 adsorption, activation, and desorption processes. Also summarized are a number of methods for enhancing the selectivity and efficiency of catalytic reactions. Lastly, the challenges and future outlook of light-driven reactions for CO2 reduction are presented.

Polylactic acid (PLA) has been extensively used in 3D printing due to its low melting temperature and minimal warping. This study investigated the reinforcement of PLA filament with soda lignin from oil palm empty fruit bunches (OPEFB) in the presence of epoxidised palm oil (EPO) as 3D printable filament. The alkaline extraction method was carried out used sodium hydroxide (NaOH), followed by precipitation with mineral acids utilising one-factor-at-a-time (OFAT). The highest extraction yield was 30 % using 1 M NaOH and 20 % phosphoric acid. Fourier transforms infra-red (FTIR) confirmed the removal of lignin in the absence of 1740 cm-1 and 1513 cm-1 peaks. Scanning electron microscopy images revealed rough surface and less voids observed at the cross-section of filament. The addition of 1 phr lignin and 5 phr EPO (PLAE1) showed improvement in thermal and mechanical properties, with higher To around 5 degrees C and 10 % crystallinity. Brittleness is reduced by 5 % for PLAE1 compared to unfilled PLA due to increase of elongation at break. Rheology analysis revealed that PLA/lignin filament appeared to be more viscous, shown by lower complex viscosity of roughly 100 Pa compared to 800 Pa for PLA at 1 rad/s. PLAE1 exhibiting decreased in brittleness by 4 %, and high impact strength of 37 %. Dynamic mechanical analysis revealed that PLAE1 had lower rigidity than unfilled PLA with lower damping factor and storage modulus of 1.67 and 1.54 GPa. The findings revealed that the PLAE1 biocomposite has potential as alternative filament for sustainable 3D printing.