Materials Science Research Articles

Agglomeration phenomenon in graphene/polymer nanocomposites: Reasons, roles, and remedies

The addition of low-loading content of nanofillers may improve the material properties of polymer-based nanocomposites. This improvement directly corresponds to the density of well-dispersed nanofillers in the matrix. However, for higher nanofiller loadings, the nanocomposites' material properties not only may not be improved but also may be degraded due to agglomeration. This complex phenomenon, where nanofillers tend to form agglomerates with the enhancement of volume fraction, poses significant challenges in materials science and nanotechnology. It has been proven that agglomerations hinder the performance of the nanocomposites and thwart the unique properties of nanofillers in most aspects. Graphene, one of the most used nanofillers, plays a remarkable role in nanotechnology. Therefore, the key focus of the current review is to provide insight into the impact of agglomeration on the various material properties such as tensile, flexural, fracture, fatigue, thermal, electrical, and barrier characteristics of the polymer nanocomposites reinforced by graphene-based structures. A comprehensive review of the factors leading to the agglomeration of graphene in the nanocomposites was presented. It was concluded that agglomeration could be a barrier to developing polymer-based nanocomposites, and the challenges of controlling the nanofiller agglomerations were discussed in depth, highlighting the issue's complexity.

Solid-Solution MXenes: Synthesis, Properties, and Applications.

ConspectusMXenes, among other two-dimensional (2D) materials such as graphene, hexagonal BN, transition metal dichalcogenides (TMDs), 2D metal-organic frameworks (MOFs), and covalent organic frameworks (COFs), are the fastest growing class discovered thus far. The general formula of MXenes is Mn+1XnTx, where M, X, and Tx represent an early transition metal (Ti, V, Nb, Mo, etc.), C and/or N, and the surface functional groups (typically, O, OH, F, Cl), respectively, and n can be between 1 and 4. MXenes as a class of materials have extraordinary properties, such as high electrical conductivity, nonlinear optical properties, solution processability, scalability and ease of synthesis, redox capability, and tunable surface properties, among others; the specific properties, however, depend on their chemistry. Since the initial report of the first MXene in 2011, the research community has primarily focused on Ti3C2Tx, and the amount of research work to investigate its synthesis and properties has increased exponentially over the years. In materials science, alloying is a useful way of synthesizing new materials to improve the properties of a class of materials. Advancement of steel and synthesis of inorganic semiconductors can be regarded as some of the major historical advancements in the concept of alloying. Thus, just one year after the initial report of MXenes, the first solid-solution MXene, (TiNb)2CTx, was reported, which demonstrates the inherent chemical tunability of this class of materials.MXenes have two sites for compositional variation: elemental substitution on both the metal (M) and carbon/nitrogen (X) sites, presenting promising routes for tailoring their properties. X-site solid-solutions include carbonitride MXenes and are the least studied class of MXenes to date. Comparatively, multi-M MXenes have acquired significant attention, leading to the extreme example of high-entropy solid-solution MXenes. By using multiple M elements, a significant expansion of the structural and chemical diversity is possible, giving rise to novel chemical, magnetic, electronic, and optical properties that cannot be accessed by single-M MXenes. Solid-solution MXenes represent the largest and most tunable class of MXenes; solid-solution MXenes are those that have multiple metals that are randomly distributed on their M sites with no distinct chemical ordering. Using multiple M elements in MXenes, it is possible to synthesize novel MXene structures that cannot be produced otherwise, such as M5X4Tx MXenes. Based on their chemistry, it is possible to rationally control the electronic, optical, mechanical, and chemical properties in a way that no other class of MXenes can. In some cases, the resultant property is linearly related to the chemistry, such as the electrical conductivity, while in other cases the properties are nonlinear or emergent: optical properties, enabling these MXenes to fulfill roles that no other MXene, or 2D material, can.In this Account, we discuss the recent progress in the synthesis, properties, applications, and outlook of solid-solution MXenes. Importantly, we demonstrate how multi-M solid-solutions can be used to tailor properties for specific applications easily.

Segmentation study of nanoparticle topological structures based on synthetic data.

Nanoparticles exhibit broad applications in materials mechanics, medicine, energy and other fields. The ordered arrangement of nanoparticles is very important to fully understand their properties and functionalities. However, in materials science, the acquisition of training images requires a large number of professionals and the labor cost is extremely high, so there are usually very few training samples in the field of materials. In this study, a segmentation method of nanoparticle topological structure based on synthetic data (SD) is proposed, which aims to solve the issue of small data in the field of materials. Our findings reveal that the combination of SD generated by rendering software with merely 15% Authentic Data (AD) shows better performance in training deep learning model. The trained U-Net model shows that Miou of 0.8476, accuracy of 0.9970, Kappa of 0.8207, and Dice of 0.9103, respectively. Compared with data enhancement alone, our approach yields a 1% improvement in the Miou metric. These results show that our proposed strategy can achieve better prediction performance without increasing the cost of data acquisition.

Mechanism of Co3O4-TiO2 Nanocomposite Formation with Enhanced Photocatalytic Performance

TiO2 nanotubes are tubular structures that have garnered significant attention in materials science and engineering due to their unique properties and diverse applications. In this study, highly ordered and well aligned TiO2 nanotubes were successfully synthesized through anodization of Ti foil in ethylene glycol (C2H6O2) containing ammonium fluoride (NH4F) and hydrogen peroxide (H2O2) at 60 V for 30 minutes. The effectiveness of TiO2 as a photocatalyst under solar light is limited by its wide band gap and high recombination rate of charge carriers. To address these limitations, TiO2 nanotubes were modified with cobalt oxide. The resulting Co3O4-TiO2 nanocomposite was synthesized using a wet impregnation technique, aiming to enhance the photocatalytic performance of TiO2 nanotubes across a broader range of the solar spectrum. The formation of Co3O4-TiO2 nanocomposite is by immersing the TiO2 nanotubes in the metal salt precursor solution of Co(NO3)2 for a certain soaking period. The soaking cycle was repeated a few times to ensure the deposition of cobalt oxide nanostructures on the TiO2 nanotube samples. This diffusion interstitial process via wet impregnation was time dependent, which altered the amount of cobalt loaded on the nanotube's surface. The addition of cobalt significantly improved the photodegradation activity of the nanotubes under visible light, outperforming bare TiO2 nanotubes. This enhancement is likely due to the cobalt acting as shallow traps, which effectively promote the separation of photogenerated charge carriers.

Numerical assessment of thermal bridging effects in 3D-printed foam concrete walls

Abstract Integrating smart technology and advanced materials in the construction industry has revolutionized traditional building practices, enhancing efficiency, sustainability, and overall performance. Researchers and professionals in the construction sector have shown significant interest in three-dimensional concrete printing (3DCP) for automating structural engineering tasks. Despite its potential as a sustainable solution to modern construction issues, there is a lack of research on the thermal insulation performance of three-dimensional printed concrete (3DPC) building envelopes, and the potential for integrating foam concrete (FC) to enhance energy efficiency has not yet been studied. This paper presents a numerical analysis examining how different infill geometries affect the thermal performance of 3D-printed foam concrete (3DPFC) lattice envelopes. Six lattice structures were designed with identical thickness, height, length, and comparable insulation areas. The effects of the contact (intersection) area of webs with the interior face shell, webs, and infill rows on the thermal performance of granularly insulated envelopes were studied. The effectiveness of insulation was also established. The findings indicate that the thermal transmittance of 3DPC envelopes correlates directly with the contact area of the webs and the interior surface, with U-values ranging from 0.151 W/m²·K to 0.652 W/m²·K. Notably, the absence of direct connections between exterior and interior surfaces enhances insulation efficiency, with double-row structures achieving up to 94% insulation efficiency. However, when there is a direct connection between the two surfaces, the thermal performance of these envelopes is mainly affected by the contact (intersection) area of the webs with the interior face rather than the number of webs. By integrating foam concrete and double-row walls, this study demonstrates an innovative approach to reducing thermal bridging and improving energy performance in 3D-printed construction. The results offer novel insight into optimizing the thermal behavior of 3DPC systems for sustainable building practices.

Piezoelectric Scaffolds as Smart Materials for Bone Tissue Engineering

Bone repair and regeneration require physiological cues, including mechanical, electrical, and biochemical activity. Many biomaterials have been investigated as bioactive scaffolds with excellent electrical properties. Amongst biomaterials, piezoelectric materials (PMs) are gaining attention in biomedicine, power harvesting, biomedical devices, and structural health monitoring. PMs have unique properties, such as the ability to affect physiological movements and deliver electrical stimuli to damaged bone or cells without an external power source. The crucial bone property is its piezoelectricity. Bones can generate electrical charges and potential in response to mechanical stimuli, as they influence bone growth and regeneration. Piezoelectric materials respond to human microenvironment stimuli and are an important factor in bone regeneration and repair. This manuscript is an overview of the fundamentals of the materials generating the piezoelectric effect and their influence on bone repair and regeneration. This paper focuses on the state of the art of piezoelectric materials, such as polymers, ceramics, and composites, and their application in bone tissue engineering. We present important information from the point of view of bone tissue engineering. We highlight promising upcoming approaches and new generations of piezoelectric materials.

Study on Failure Characteristics and Acoustic Emission Laws of Rock-like Specimens under Uniaxial Compression

Complex underground conditions make it challenging to conduct extensive coring, and it is difficult for laboratories to carry out a large number of rock mechanics experiments due to the limited number of cores. Rock-like specimens are commonly used in the laboratory to replace coal-rock specimens. In this paper, rock-like specimens with different proportions are produced to study the mechanical properties, failure characteristics, and acoustic emission laws of rock-like specimens under uniaxial compression. The results show that when the rock-like specimen does not contain gypsum, the stress of the specimen decreases rapidly after reaching the peak stress, which is similar to the mechanical properties of hard brittle rock. When the rock-like specimen contains gypsum, the stress of the specimen decreases slowly after reaching the compressive limit, and the failure of the specimen is gentle, which is similar to the mechanical properties of soft rock. When the specimen lacks gypsum, the strength of the rock-like specimen is larger, and the strength of the specimen is positively correlated with the proportion of cement. When the specimen contains gypsum, the strength of the rock-like specimen decreases sharply, and the strength of the rock-like specimen is negatively correlated with the proportion of gypsum. The maximum acoustic emission ringing count increases with higher cement content but decreases with increased gypsum content. The cumulative count change of acoustic emissions approximately underwent four stages, aligning well with the four stages of uniaxial compression failure observed in typical rock. The research results have important reference value for the selection of rock-like materials to replace the original rock materials for laboratory research.

A Room‐Temperature Lithium‐Restocking Strategy for the Direct Reuse of Degraded LiFePO4 Electrodes

The sustainable development of lithium iron phosphate (LFP) batteries calls for efficient recycling technologies for spent LFP (SLFP). Even for the advanced direct material regeneration (DMR) method, multiple steps including separation, regeneration, and electrode refabrication processes are still needed. To circumvent these intricacies, new regeneration methods that allow direct electrode reuse (DER) by rejuvenating SLFP electrodes without damaging its structure are desired. Here, a 0.1 M lithium triethyl borohydride/tetrahydrofuran solution, which has the proper reductive capability to reduce Fe3+ in SLFP to Fe2+ without alloying with the aluminum current collector, is selected as the lithiation/regeneration reagent to restock the Li loss and regenerate SLFP electrodes. By soaking the SLFP electrodes in the lithiation solution, we successfully rejuvenated the crystal structure and electrochemical activity of SLFP electrodes with structural integrity within only 6 minutes at room temperature. When being directly reused, the regenerated LFP electrodes deliver a high specific capacity of 162.6 mAh g‐1 even after being exposed to air for 3 months. The DER strategy presents significant economic and environmental benefits compared with the DMR method. This research provides a timely and innovative solution for recycling spent blade batteries using large‐sized LFP electrodes, boosting the closed‐loop development of LFP batteries.

A Room-Temperature Lithium-Restocking Strategy for the Direct Reuse of Degraded LiFePO4 Electrodes.

The sustainable development of lithium iron phosphate (LFP) batteries calls for efficient recycling technologies for spent LFP (SLFP). Even for the advanced direct material regeneration (DMR) method, multiple steps including separation, regeneration, and electrode refabrication processes are still needed. To circumvent these intricacies, new regeneration methods that allow direct electrode reuse (DER) by rejuvenating SLFP electrodes without damaging its structure are desired. Here, a 0.1 M lithium triethyl borohydride/tetrahydrofuran solution, which has the proper reductive capability to reduce Fe3+ in SLFP to Fe2+ without alloying with the aluminum current collector, is selected as the lithiation/regeneration reagent to restock the Li loss and regenerate SLFP electrodes. By soaking the SLFP electrodes in the lithiation solution, we successfully rejuvenated the crystal structure and electrochemical activity of SLFP electrodes with structural integrity within only 6 minutes at room temperature. When being directly reused, the regenerated LFP electrodes deliver a high specific capacity of 162.6 mAh g-1 even after being exposed to air for 3 months. The DER strategy presents significant economic and environmental benefits compared with the DMR method. This research provides a timely and innovative solution for recycling spent blade batteries using large-sized LFP electrodes, boosting the closed-loop development of LFP batteries.

The 40th anniversary of the School of Materials Science and Engineering at Southeast University

The 40th anniversary of the School of Materials Science and Engineering at Southeast University

Influence of Simulations on Students' Achievement in Physics in Secondary Schools in Nandi East, Kenya

Many secondary schools in Kenya are yet to incorporate Mobile Technologies into their educational practices, despite widespread ownership and use by both students and teachers. This study investigated the influence of simulations on students' achievement in physics in secondary schools in Nandi East, Kenya. The dismal performance in physics locally and nationally may be linked to slow technological integration, hence the need for the study. The target population consisted of Form Three physics students and 30 Heads of Department (HODs) from 31 secondary schools. A descriptive survey design was utilized. Random and purposive sampling was used to select participants, and data collection involved questionnaires and interviews. Using Taro Yamane’s Formula, the sample size was determined to be 300 students, 30 HODs, and 30 schools. Data were analyzed using SPSS, providing descriptive statistics such as frequencies, percentages, pie charts, means, and standard deviations (SD). The reliability of the questionnaire items was confirmed with a Cronbach Alpha coefficient above 0.7. The findings emphasize the significant benefits of integrating simulations on students' academic achievement in secondary school physics learning. While the study offers important insights, its geographical limitation, technological access issues, and narrow scope constraints must be considered. These results have implications for various stakeholders including learners, parents, teachers, school administrators, curriculum developers, and policymakers. Learners will benefit from improved effectiveness, engagement, and personalized instruction, while policymakers will gain insights for future improvements. The study recommended learners be allowed to bring their own devices to school to ensure equality and equity in access to digital learning materials.

The Dispersion-Strengthening Effect of TiN Nanoparticles Evoked by Ex Situ Nitridation of Gas-Atomized, NiCu-Based Alloy 400 in Fluidized Bed Reactor for Laser Powder Bed Fusion

Throughout recent years, the implementation of nanoparticles into the microstructure of additively manufactured (AM) parts has gained great attention in the material science community. The dispersion strengthening (DS) effect achieved leads to a substantial improvement in the mechanical properties of the alloy used. In this work, an ex situ approach of powder conditioning prior to the AM process as per a newly developed fluidized bed reactor (FBR) was applied to a titanium-enriched variant of the NiCu-based Alloy 400. Powders were investigated before and after FBR exposure, and it was found that the conditioning led to a significant increase in the TiN formation along grain boundaries. Manufactured to parts via laser-based powder bed fusion of metals (PBF-LB/M), the ex situ FBR approach not only revealed a superior microstructure compared to unconditioned parts but also with respect to a recently introduced in situ approach based on a gas atomization reaction synthesis (GARS). A substantially higher number of nanoparticles formed along cell walls and enabled an effective suppression of dislocation movement, resulting in excellent tensile, creep, and fatigue properties, even at elevated temperatures up to 750 °C. Such outstanding properties have never been documented for AM-processed Alloy 400, which is why the demonstrated FBR ex situ conditioning marks a promising modification route for future alloy systems.

RETRACTION: High-Resolution Imaging of Human Viruses in Liquid Droplets.

G. M. Jonaid, W. J. Dearnaley, M. A. Casasanta, L. Kaylor, S. Berry, M. J. Dukes, M. S. Spilman, J. L. Gray and D. F. Kelly, "High-Resolution Imaging of Human Viruses in Liquid Droplets," Advanced Materials 33, no. 37 (2021): 2103221, https://doi.org/10.1002/adma.202103221. The above article, published online on 24 July 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the author, M. S. Spilman; the journal Editors-in-Chief, Irem Bayindir-Buchhalter and Esther Levy; and Wiley-VCH GmbH. The retraction has been agreed upon following a thorough peer review process conducted by the author's institution, Penn State University. The peer review determined that there is no significant correlation between the EM map and the model at any resolution. Furthermore, the validation curves and the published curves significantly misrepresent the fit of models to EM maps. The authors didnot provide their original research data, EM maps, and models included in the study, to justify their findings. As a result, the institution recommends the article be retracted. The editors of the journal agree with these findings as they are supported by their own independent investigation. The author, D.F. Kelly, disagrees with the retraction; G.M. Jonaid agrees with the retraction; the other authors were unavailable for comment.

Nonlinear vibration and parametric excitation of magneto‐thermo elastic embedded nanobeam using homotopy perturbation technique

AbstractThe present study focuses on the modeling and analyzing the nonlinear vibration patterns and parametric excitation of embedded Euler–Bernoulli nanobeams subjected to thermo‐magneto‐mechanical loads. The Euler–Bernoulli nanobeam is developed with external parametric excitation. The governing equation of motion is derived by utilizing nonlocal continuum theory and nonlinear von Karman beam theory. Subsequently, the homotopy perturbation technique is employed to determine the vibration frequencies. Finally, the modulation equation of Euler–Bernoulli nanobeams is derived for simply supported boundary condition. In order to validate our findings, we conduct a comparative analysis against existing literature, thereby underscoring the effectiveness and robustness of our proposed methodology. The influence of stress, magnetic potential, temperature, damping coefficient, Winkler coefficient, and nonlocal parameters are tested numerically on nonlinear frequency‐amplitude and parametric excitation–amplitude responses. The numerical examples indicate the significant impact of physical variables on the nonlinear frequency and parametric excitation. The primary objective of this study is to investigate the effects of external physical variables on the dynamic behavior of nanobeams, particularly in nonlinear regimes. This study provides insights into the design and control of nanostructures under complex load conditions, contributing to developing advanced materials and nanosystems.

Unveiling the Microscopic Origin of Ultrahigh Initial Coulombic Efficiency of High-Entropy Metal-Organic Frameworks for Sodium Storage.

The initial irreversible capacity loss during the first charging process largely reduces the affordable energy and power density of sodium storage devices, and developing advanced materials is the efficient way to solve this problem, which is fraught with challenges. Herein, inspired by theoretical calculations and the high-entropy concept, a series of fewer layers of high-entropy metal-organic frameworks (FLHE-MOFs) are successfully fabricated, delivering an ultrahigh initial Coulombic efficiency (ICE) of 86.1% and excellent cycling performance, which is far more than that of the other electrode materials (generally <70%). Greatly, the storage behavior of high-, medium-, and low-entropy MOFs is clarified by theoretical calculations and in-/ex-situ characterization, revealing that Co and Fe species can provide substantial sodium storage sites and largely enhance the charge transfer rate, whereas high-entropy effect can enable structural reversibility. Sodium ion capacitors constructed with FLHE-MOFs as the anode can provide an ultrahigh energy density of 121.8 W h kg-1 (200 W kg-1) and an extremely long-term cycle lifespan. This work not only breaks the limitation of MOF materials with poor performance for sodium storage but also provides an effective strategy for the fabrication and application of high-performance MOF-based anode materials with high ICE, in which this idea may also be applied in other fields.

Self-Locking in Collapsed Carbon Nanotube Stacks via Molecular Dynamics

Self-locking structures are often studied in macroscopic energy absorbers, but the concept of self-locking can also be effectively applied at the nanoscale. In particular, we can engineer self-locking mechanisms at the molecular level through careful shape selection or chemical functionalisation. The present work focuses on the use of collapsed carbon nanotubes (CNTs) as self-locking elements. We start by inserting a thin CNT into each of the two lobes of a collapsed larger CNT. We aim to create a system that utilises the unique properties of CNTs to achieve stable configurations and enhanced energy absorption capabilities at the nanoscale. We used molecular dynamics simulations to investigate the mechanical properties of periodic systems realised with such units. This approach extends the application of self-locking mechanisms and opens up new possibilities for the development of advanced materials and devices.

Predictive Modeling of High-Entropy Alloys and Amorphous Metallic Alloys Using Machine Learning.

High entropy alloys and amorphous metallic alloys represent two distinct classes of advanced alloy materials, each with unique structural characteristics. Their emergence has garnered considerable interest across the materials science and engineering communities, driven by their promising properties, including exceptional strength. However, their extensive compositional diversity poses substantial challenges for systematic exploration, as traditional experimental approaches and high-throughput calculations struggle to efficiently navigate this vast space. While the recent development in data-driven materials discovery could potentially help, such efforts are hindered by the scarcity of comprehensive data and the lack of robust predictive tools that can effectively link alloy composition with specific properties. To address these challenges, we have deployed a machine-learning-based workflow for feature selection and statistical analysis to afford predictive models that accelerate the data-driven discovery and optimization of these advanced materials. Our methodology is validated through two case studies: (i) a regression analysis of the bulk modulus, and (ii) a classification analysis based on glass-forming ability. The Bayesian-optimized regression model trained for the prediction of bulk modulus achieved an R2 of 0.969, an mean absolute error (MAE) of 3.958 GPa, and an root mean square error (RMSE) of 5.411 GPa, while our classification model for predicting glass-forming ability achieved an F1-score of 0.91, an area-under-the-curve of the receiver-operating-characteristic curve of 0.98, and an accuracy of 0.91. Furthermore, by leveraging a wide array of chemical data from diverse literature sources, we have successfully predicted a broad range of properties. This success underscores the efficacy of our modeling approach and emphasizes the importance of a comprehensive feature analysis and judicious feature selection strategy over a mere reliance on complex modeling techniques.

Experimental studies on insulating oils for power transformer applications

Abstract In recent days, ester-based alternatives like natural and synthetic oils are preferred as an alternative to mineral oil by the power industry due to its higher bio-degradability, superior thermal conductivity, and fire resistance than mineral oil. In addition to this, blended oils are quite attractive because of its low dielectric loss and a low ratio of degradation. The present research deals with the experimental studies on different insulating oils like mineral oil, synthetic ester oil, coconut oil, blended oils, and nanofluids, which are developed from ZnO and TiO2 nanoparticle doped at 0.01 vol% in different insulating oils through ultrasonication process. In this experiment, the critical properties like electrical and physicochemical properties are examined according to International Electrotechnical Commission and American Society for Testing and Materials standards. Results show that ZnO and TiO2 nano-powder enhances the AC breakdown voltage (BDV), dielectric permittivity, and DC resistivity properties of different insulating oils with improved flash and fire resistance. Remarkably, it is found that mineral oil-based TiO2 nanofluid shows an enhancement of 55.8 % in AC BDV, 9.1 % in permittivity, and 15 % in flash resistance. Moreover, mineral oil based ZnO and TiO2 nanofluid results exhibit remarkable decrement in loss tangent with increased DC resistance property. Finally, it is observed that the influence of TiO2 nano-powder at 0.01 vol% remarkably enhances the electrical and physicochemical properties in different insulating oils compared to ZnO nanofluids. Hence, mineral oil and green insulating based synthetic ester oil (which is based on TiO2 nanofluids) have been considered as a potential candidate to make alternatives to traditional insulating oil for power transformer applications. The findings offer critical insights for the future of transformer insulation systems, guiding industry standards and fostering innovation in material science.

Unveiling the High-Pressure Behavior of Thymol+Carvone NADES: A Combined Experimental-Computational Approach

this study considers the high-pressure behavior of thymol + carvone Natural Deep Eutectic Solvent using a combined experimental and computational approach. Experimentally, PVT (pressure – volume/density - temperature) measurements were conducted to characterize the fluid volumetric behavior as well as compressibility and internal pressure, which are directly related with hydrogen bonding and nanoscopic structuring. Likewise, these measurements provide crucial insights into the thermodynamic properties of the considered fluid under high pressure, which is pivotal for scaling up and process design oh high pressure operations. Computationally, the PC-SAFT equation of state was employed to predict phase equilibria and PVT behavior, Machine Learning for density predictions, while Density Functional Theory and Classical Molecular Dynamics simulations were considered for the structural and dynamic characterization. These simulations provides insights into the electronic structure, intermolecular interactions (hydrogen bonding), liquid structuring and they unveils the pressure's impact on microscopic interactions, structural organization, and transport properties. This comprehensive investigation aims to shed light on the behavior under high pressure, facilitating their optimization for applications in chemical processing, energy storage, and materials science.

Self-Assembling Peptides for Vaccine Adjuvant Discovery

Vaccination is credited as a significant medical achievement contributing to the decline in morbidity and mortality of infectious diseases. Traditional vaccines composed of inactivated and live-attenuated whole pathogens confer the induction of potent and long-term immune responses; however, traditional vaccines pose a high risk of eliciting autoimmune and allergic responses as well as inflammations. New modern vaccines, such as subunit vaccines, employ minimum pathogenic components (such as carbohydrates, proteins, or peptides), overcome the drawbacks of traditional vaccines and stimulate effective immunity against infections. However, the low immunogenicity of subunit vaccines requires effective immune stimulants (adjuvants), which are an indispensable factor in vaccine development. Although there are several approved adjuvants in human vaccines, the challenges of matching and designing appropriate adjuvants for specific vaccines, along with managing the side effects and toxicity of existing adjuvants in humans, are driving the development of new adjuvants. Self-assembling peptides are a promising biomaterial rapidly emerging in the fields of biomedicine, vaccination and material science. Here, peptides self-assemble into ordered supramolecular structures, forming different building blocks in nanoparticle size, including fibrils, tapes, nanotubes, micelles, hydrogels or nanocages, with great biostability, biocompatibility, low toxicity and effectiveness at controlled release. Self-assembling peptides are effective immunostimulatory agents used in vaccine development to enhance and prolong immune responses. This review describes the predominant structures of self-assembling peptides and summarises their recent applications as vaccine adjuvants. Challenges and future perspectives on self-assembled peptides as vaccine adjuvants are also highlighted.