Surface modification of polymeric biomaterials using plasma has emerged as an effective strategy to optimize the cell-material interface without compromising the structural properties of the material. This work presents a critical review of the impact of low-temperature plasma treatment on enhancing cell adhesion, with emphasis on the physicochemical changes induced on the surface of polymers commonly used in biomedical applications. The mechanisms of interaction between reactive plasma species and the polymer surface are analyzed, along with techniques used to introduce hydrophilic functional groups that improve wettability and biocompatibility. Scientific evidence demonstrates that this type of surface modification promotes greater cell spreading, anchorage, and proliferation, making it particularly useful in the design of tissue engineering scaffolds, implantable devices, and vascular prostheses. Finally, current and future trends in the development of smart plasma-functionalized biomaterials are discussed, highlighting their role in regenerative medicine.
Functional amyloid fibrils, once primarily associated with amyloidosis, are now recognized for their exceptional potential as biomaterials due to their unique structural features, including remarkable mechanical strength, high stability, and self-assembly capabilities. This review highlights their transformative applications across a wide range of industries, from cutting-edge drug delivery systems and next-generation biosensors to tissue engineering, surface technologies, energy storage, and environmental solutions. Their versatility extends into innovative sectors like information transfer systems, cell adhesion, protein fusion, food technology, and novel catalytic systems. Despite significant progress, critical gaps remain in the research. This review not only consolidates current applications but also underscores the vast potential for future advancements, positioning functional amyloids as key players in emerging biomaterials technologies.
We propose a modular, fast and large-area fabrication of bio-piezoelectric films. The technique is based on the formation of cone-jet mode by applying a high voltage electric field to conductive spiked metal disks. And the self-assembly process of biomolecular materials through nanoconfinement with in-situ poling effect. This job achieved print speeds of up to 9.2 109 um3/s with a combination of only 2 printheads. At the same time, the modular design allows the MLSP to achieve theoretically unlimited print efficiency. It also provides flexible configuration options for different printing needs, such as preparing films of different areas and shapes. In short, MLSP demonstrates the ability of piezoelectric biomaterials to undergo ultra-fast, large-scale assembly. Demonstrates good potential as a universally applicable bio-device for the fabrication of bio-piezoelectric films
The biomaterials exploitation in a sophisticated manner can provide extensive opportunities for experimentation in the field of interdisciplinary and multidisciplinary scientific research. Owing to the unique features of this trendy area, research scientists have been directed/redirected their interests in bio-based biomaterials for targeted applications in different sectors of the modern world. The present manuscript highlights the novel perspectives of biomaterials as a trendy source to engineer functional entities in numerous geometries for pharmaceuticals, cosmeceuticals, nutraceuticals, and other biotechnological or biomedical applications.
Multienzyme cascaded reactions are widely utilized because they can generate value-added biomaterials and biodevices from simple raw materials. However, how to promote the catalytic efficiency and synergistic effect of the multienzyme system is proved to be a challengeable point. Recent discovery repeatedly emphasized the strategy of assembled multienzyme complexes or forming subcellular compartments for spacial optimization. This highly ordered and tunable organization contributes to various biochemical processes. This dissertation focuses mainly on analysis and progresses in this cascaded strategy, regarding the feasibility of regional compartments for natural or artificial biochemical reactions in vivo and vitro, simultaneously.
Hypothesis The treatment of bone fractures still represents a challenging clinical issue when complications due to impaired bone remodelling (i.e. osteoporosis) or infections occur. These clinical needs still require a radical improvement of the existing therapeutic approach through the design of advanced biomaterials combining the ability to promote bone regeneration with anti-fouling/anti-adhesive properties able to minimise unspecific biomolecules adsorption and bacterial adhesion. Strontium-containing mesoporous bioactive glasses (Sr-MBG), able to exert a pro-osteogenic effect by releasing Sr2+ ions, have been successfully functionalised to provide mixed-charge surface groups with low-fouling abilities. Experiments Sr-MBG have been post-synthesis modified by co-grafting hydrolysable short chain silanes containing amino (aminopropylsilanetriol) and carboxylate (carboxyethylsilanetriol) moieties to achieve a zwitterionic zero-charge surface and then characterised in terms of textural-structural properties, bioactivity, cytotoxicity, pro-osteogenic and low-fouling capabilities. Findings After zwitterionization the in vitro bioactivity is maintained, as well as the ability to relea
Mechanical forces such as fluid shear have been shown to enhance cell growth and differentiation, but knowledge of their mechanistic effect on cells is limited because the local flow patterns and associated metrics are not precisely known. Here we present real-time, noninvasive measures of local hydrodynamics in 3D biomaterials based on nuclear magnetic resonance. Microflow maps were further used to derive pressure, shear and fluid permeability fields. Finally, remodeling of collagen gels in response to precise fluid flow parameters was correlated with structural changes. It is anticipated that accurate flow maps within 3D matrices will be a critical step towards understanding cell behavior in response to controlled flow dynamics.
Despite the advantages of using biodegradable metals in implant design, their uncontrolled degradation and release remain a challenge in practical applications. A validated computational model of the degradation process can facilitate the tuning of implant biodegradation by changing design properties. In this study, a physicochemical model was developed by deriving a mathematical description of the chemistry of magnesium biodegradation and implementing it in a 3D computational model. The model parameters were calibrated using the experimental data of hydrogen evolution by performing a Bayesian optimization routine. The model was validated by comparing the predicted change of pH in saline and buffered solutions with the experimentally obtained values from corrosion tests, showing maximum 5% of difference, demonstrating the model's validity to be used for practical cases.
In this paper, we present an approach for modeling bio-tissues that incorporates the variability in properties as part of their characteristics. This is achieved by considering the parameters of the model of a biomaterial to themselves be random variables and represented by a probability distribution over the space of parameters. This probability distribution is obtained by the systematic use of Bayesian inference together with a continuum mechanics based solution of a boundary value problem. We illustrate this approach by characterizing sheep arteries by using a combination of experimental data and different hyperelastic models. Furthermore, we also develop a model based Bayesian classification of new data into different classes based on the computed model parameter probability distribution.
Sacrificial bonds and hidden length in structural molecules account for the greatly increased fracture toughness of biological materials compared to synthetic materials without such structural features, by providing a molecular-scale mechanism for energy dissipation. One example is in the polymeric glue connection between collagen fibrils in animal bone. In this paper, we propose a simple kinetic model that describes the breakage of sacrificial bonds and the release of hidden length, based on Bell's theory. We postulate a master equation governing the rates of bond breakage and formation. This enables us to predict the mechanical behavior of a quasi-one-dimensional ensemble of polymers at different stretching rates. We find that both the rupture peak heights and maximum stretching distance increase with the stretching rate. In addition, our theory naturally permits the possibility of self-healing in such biological structures.
Large particle sorters have potential applications in sorting microplastics and large biomaterials (>50 micrometer), such as tissues, spheroids, organoids, and embryos. Though great advancements have been made in image-based sorting of cells and particles (<50 micrometer), their translation for high-throughput sorting of larger biomaterials and particles (>50 micrometer) has been more limited. An image-based detection technique is highly desirable due to richness of the data (including size, shape, color, morphology, and optical density) that can be extracted from live images of individualized biomaterials or particles. Such a detection technique is label-free and can be integrated with a contact-free actuation mechanism such as one based on traveling surface acoustic waves (TSAWs). Recent advances in using TSAWs for sorting cells and particles (<50 micrometer) have demonstrated short response times (<1 ms), high biocompatibility, and reduced energy requirements to actuate. Additionally, TSAW-based devices are miniaturized and easier to integrate with an image-based detection technique. In this work, a high-throughput image-detection based large particle microfluidic
The past decade has seen unprecedented growth in active matter and autonomous biomaterials research, yielding diverse classes of materials that promise revolutionary applications such as self-healing infrastructure and self-sensing tissue implants. However, inconsistencies in metrics, definitions, and analysis algorithms across research groups, as well as the high-dimension data streams, has hindered identification of performance intersections. Progress in this arena demands multi-disciplinary team approaches to discovery with scaffolded training and cross-pollination of ideas, and requires new learning and collaboration methods. To address this challenge, we have developed a hackathon platform to train future scientists and engineers in big data, interdisciplinary collaboration, and community coding; and to design and beta-test high-throughput (HTP) biomaterials analysis software and workflows. We enforce a flat hierarchy, pairing participants ranging from high school students to faculty with varied experiences and skills to collectively contribute to data acquisition and processing, ideation, coding, testing and dissemination. With clearly-defined goals and deliverables, particip
Learning to generate images with internally repeated and periodic structures poses a fundamental challenge for machine learning and computer vision models, which are typically optimised for local texture statistics and semantic realism rather than global structural consistency. This limitation is particularly pronounced in applications requiring strict control over repetition scale, spacing, and boundary coherence, such as microtopographical biomaterial surfaces. In this work, biomaterial design serves as a use case to study conditional generation of repeated patterns under weak supervision and class imbalance. We propose DF-ACBlurGAN, a structure-aware conditional generative adversarial network that explicitly reasons about long-range repetition during training. The approach integrates frequency-domain repetition scale estimation, scale-adaptive Gaussian blurring, and unit-cell reconstruction to balance sharp local features with stable global periodicity. Conditioning on experimentally derived biological response labels, the model synthesises designs aligned with target functional outcomes. Evaluation across multiple biomaterial datasets demonstrates improved repetition consistenc
Viscoelastic rate-type fluid models are essential for describing the behavior of a wide range of complex materials, with applications in fields such as engineering, biomaterials, and medicine. These models are particularly useful for understanding the rheological properties of materials that exhibit both elastic and viscous behavior under deformation. However, many real-world applications involve significant thermal effects, where heat conduction and the temperature dependence of material properties must also be considered. In this paper, we introduce a thermodynamically consistent model for heat-conducting viscoelastic rate-type fluids and establish the existence of a global weak solution in a two-dimensional setting. The result holds under the condition that the initial energy and entropy are controlled in appropriate natural norms.
I present a concise, first principles metrological framework for imaging dielectric biomaterials by probing the full phase space (Wigner) distribution of a quantum electromagnetic field. Building on a rigorous multilayer Maxwell and Cole Cole model for stratified tissue, my method (Quantum Phase space Tomography, QPST) couples analytical forward theory with quantum metrology and Bayesian inference. I prepare a structured quantum EM probe (e.g. a squeezed microwave pulse) that interacts with tissue and then perform full quantum state tomography of the outgoing field. The recovered Wigner quasi probability reveals subwavelength and non classical features lost in classical imaging. By projecting the measurement onto the analytically derived tissue response manifold, I recover key physiological parameters (e.g. layer thickness, dispersion). I further define a Dielectric Anaplasia Metric (DAM) that quantifies tissue microstructural heterogeneity (e.g. malignancy) via deviations in Cole Cole parameters. My design leverages state of the art quantum sensors (e.g. NV diamond magnetometers) and advanced inverse algorithms (physics informed neural networks, diffusion priors). Numerical exampl
Active, responsive, nonequilibrium materials, at the forefront of materials engineering, offer dynamical restructuring, mobility and other complex life-like properties. Yet, this enhanced functionality comes with significant amplification of the size and complexity of the datasets needed to characterize their properties, thereby challenging conventional approaches to analysis. To meet this need, we present BARCODE (Biomaterial Activity Readouts to Categorize, Optimize, Design and Engineer), an open-access software that automates high throughput screening of microscopy video data to enable nonequilibrium material optimization and discovery. BARCODE produces a unique fingerprint or barcode of performance metrics that visually and quantitatively encodes dynamic material properties with minimal file size. Using three complementary material agnostic analysis branches, BARCODE significantly reduces data dimensionality and size, while providing rich, multiparametric outputs and rapid tractable characterization of activity and structure. We analyze a series of datasets of cytoskeleton networks and cell monolayers to demonstrate the ability of BARCODE to accelerate and streamline screening
Hierarchical biomaterials embody nature's intricate design principles, offering advanced functionalities through the complex, multi-level organization of their molecular and nanosized building blocks. However, the comprehensive characterization of their 3D structure remains a challenge, particularly due to radiation damage caused by conventional X-ray- and electron-based imaging techniques, as well as due to the length scale limitations of scattering-based investigation methods. Here, we present a study utilizing multi-directional dark-field neutron imaging in tomographic mode to visualize the 3D nanoarchitecture of nanocellulose solid foams, a class of sustainable materials possessing complex and highly tunable hierarchical structures. By exploiting the unique properties of neutrons as a probe, this non-destructive method circumvents the inherent limitations of damage-inducing ionizing radiation, preserving the structural and chemical integrity of the biomaterials, and allowing for truly multiscale characterization of the spatial orientation and distribution of cellulose nano fibrils within large-volume samples. In particular, the study showcases the 3-dimensional anisotropic orie
Biomaterial surface engineering and integrating cell-adhesive ligands are crucial in biological research and biotechnological applications. The interplay between cells and their microenvironment, influenced by chemical and physical cues, impacts cellular behavior. Surface modification of biomaterials profoundly affects cellular responses, especially at the cell-surface interface. This work focuses on enhancing cellular activities through material manipulation, emphasizing silanization for further functionalization with bioactive molecules like RGD peptides to improve cell adhesion. The grafting of three distinct silanes onto silicon wafers using both spin coating and immersion methods was investigated. This study sheds light on the effects of different alkyl chain lengths and protecting groups on cellular behavior, providing valuable insights into optimizing silane-based self-assembled monolayers (SAMs) before peptide or protein grafting for the first time. Specifically, it challenges the common use of APTES molecules in this context. These findings advance our understanding of surface modification strategies, paving the way for tailoring biomaterial surfaces to modulate cellular b
Healthcare materials, whether they are natural or synthetic, are complex structures made up of simpler materials. Because of their intricate structure, composite materials are ideal for prosthetics because it is possible to tune their structure to get mechanical properties that are compatible with bone, thus encouraging biointegration. To be effective, implants must be properly suited to the host, which necessitates complete control over both the design of the implants as well as their evolution over time as they are used. In the case of composite implants, this means that, while material control at the macroscopic scale is required during implant shaping, the quality of the interface, which is determined by phenomena acting at the nanoscale, is also required. In this work, we show that such an issue can be resolved by resorting to a multi-scale approach and that, in this context, numerical modeling is a useful tool. Then we describe the principle of the process for investigating composite biomaterials using numerical modeling. Then, we give a concrete example of the protocol by talking about the first step of the ''grafting from'' method that Professors H. Palkowski and A. Carrado
The growing interest in bio-inspired materials is driven by the need for increasingly targeted and efficient devices that also have a low ecological impact. These devices often use specially developed materials (e.g., polymers, aptamers, monoclonal antibodies) capable of carrying out the process of recognizing and capturing a specific target in a similar way to biomaterials of natural origin. In this article, we present two case studies, in which the target is a biomolecule of medical interest, in particular, α-thrombin and cytokine IL-6. In these examples, different biomaterials are compared to establish, with a theoretical-computational procedure known as proteotronics, which of them has the greatest potential for use in a biodevice.