The field of tissue engineering is constantly evolving as it aims to develop bioengineered and functional tissues and organs for repair or replacement. Due to their large surface area and ability to interact with proteins and peptides, graphene oxides offer valuable physiochemical and biological features for biomedical applications and have been successfully employed for optimizing scaffold architectures for a wide range of organs, from the skin to cardiac tissue. This review critically focuses on opportunities to employ protein–graphene oxide structures either as nanocomposites or as biocomplexes and highlights the effects of carbonaceous nanostructures on protein conformation and structural stability for applications in tissue engineering and regenerative medicine. Herein, recent applications and the biological activity of nanocomposite bioconjugates are analyzed with respect to cell viability and proliferation, along with the ability of these constructs to sustain the formation of new and functional tissue. Novel strategies and approaches based on stem cell therapy, as well as the involvement of the extracellular matrix in the design of smart nanoplatforms, are discussed.

The present review outlines the most recent advance in the field of anticorrosive coatings based on graphene oxide nanostructures as active filler. This carbonaceous material was extensively used in the last few years due to its remarkable assets and proved to have a significant contribution to composite materials. Concerning the graphene-based coatings, the synthesis methods, protective function, anticorrosion mechanism, feasible problems, and some methods to improve the overall properties were highlighted. Regarding the contribution of the nanostructure used to improve the capability of the material, several modification strategies for graphene oxide along with the synergistic effect exhibited when functionalized with other compounds were mainly discussed.

Nanostructures are more and more evolved through extensive research on their functionalities; thus, the aim of this study was to obtain layered clay–graphene oxide nanohybrids with application as reinforcing agents in polyurea nanocomposites with enhanced thermal–mechanical and fire-retardant properties. Montmorillonite (MMT) was combined with graphene oxide (GO) and amine functionalized graphene oxide (GOD) through a new cation exchange method; the complex nanostructures were analyzed through FTIR and XPS to assess ionic interactions between clay layers and GO sheets by C1s deconvolution and specific C sp3, respective/ly, C-O secondary peaks appearance. The thermal decomposition of nanohybrids showed a great influence of MMT layers in TGA, while the XRD patterns highlighted mutual MMT and GO sheets crystalline-structure disruption by the d (002) shift 2θ = 6.29° to lower values. Furthermore, the nanohybrids were embedded in the polyurea matrix, and the thermo-mechanical analysis gave information about the stiffness of MMT–GO nanocomposites, while GOD insertion within the MMT layers resulted in a 30 °C improvement in the Tg of hard domains, as shown in the DSC study. The micro CT analysis show good dispersion of inorganic structures within the polyurea, while the SEM fracture images revealed smooth surfaces. Cone calorimetry was used to evaluate fire-retardant properties through limiting the oxygen index, and MMT–GOD based nanocomposites showed a 35.4% value.

Due to environmental concerns, as well as its exceptional physical and mechanical capabilities, biodegradability, and optical and barrier qualities, nanocellulose has drawn a lot of interest as a source of reinforcing materials that are nanometer sized. This article focuses on how to manufacture cellulose nanomaterials from cotton by using different types of acids such as H₂SO₄ and HCI in different concentrations and in the presence of enzymes such as cellulase and xylanase. Two different types of bleaching methods were used before acid and enzyme hydrolysis. In the first method, cellulose was extracted by bleaching the cotton with H₂O₂. In the second method, NaOCl was utilized. For both methods, different concentrations of acids and enzymes were used to isolate nanocellulose materials, cellulose nanocrystals (CNC), and cellulose nanofibrils (CNF) at different temperatures. All obtained nanocellulose materials were analyzed through different techniques such as FT-IR, Zeta potentials, DLS, Raman spectroscopy, TGA, DSC, XRD, and SEM. The characteristic signals related to cellulose nanocrystals (CNC) were confirmed with the aid of Raman and FT-IR spectroscopy. According to the XRD results, the samples’ crystallinity percentages range from 54.1% to 63.2%. The SEM image showed that long fibers break down into small fibers and needle-like features are seen on the surface of the fibers. Using different types of bleaching has no significant effect on the thermal stability of samples. The results demonstrate a successful method for synthesizing cellulose nanofibrils (CNF) from cotton through enzymatic hydrolysis, but the results also demonstrated that the choice of bleaching method has a significant impact on the hydrodynamic properties and crystallinity of both CNC and CNF samples.

In the present work, solvent casting and Pickering emulsion methods are studied to enhance the mechanical properties of polylactic acid (PLA) composites containing surface-modified cellulose nanomaterials. To enhance the compatibility and the adhesion at the interface, cellulose nanocrystal (CNC) was functionalized by 2,4-methylene diphenyl diisocyanate (MDI) and castor oil. Their results demonstrated that the Pickering emulsion method led to better dispersion of CNC in composites, resulting in improved tensile strength, flexibility, and thermal stability (compared with solvent-casted ones). In particular, the tensile strength increased by 20% and the crystallinity increased by 15% using the Pickering emulsion technique, indicating their potential as a new generation of sustainable packaging. The findings of this research could help in creating eco-friendly packaging options by improving the mechanical features of biodegradable composites and exploring potential alternatives to overcome their limitations.

Over the past three decades, the biodegradable polymer known as poly(propylene fumarate) (PPF) has been the subject of numerous research due to its unique properties. Its biocompatibility and controllable mechanical properties have encouraged numerous scientists to manufacture and produce a wide range of PPF-based materials for biomedical purposes. Additionally, the ability to tailor the degradation rate of the scaffold material to match the rate of new bone tissue formation is particularly relevant in bone tissue engineering, where synchronized degradation and tissue regeneration are critical for effective healing. This review thoroughly summarizes the advancements in different approaches for PPF and PPF-based composite scaffold preparation for bone tissue engineering. Additionally, the challenges faced by each approach, such as biocompatibility, degradation, mechanical features, and crosslinking, were emphasized, and the noteworthy benefits of the most pertinent synthesis strategies were highlighted. Furthermore, the synergistic outcome between tissue engineering and artificial intelligence (AI) was addressed, along with the advantages brought by the implication of machine learning (ML) as well as the revolutionary impact on regenerative medicines. Future advances in bone tissue engineering could be facilitated by the enormous potential for individualized and successful regenerative treatments that arise from the combination of tissue engineering and artificial intelligence. By assessing a patient’s reaction to a certain drug and choosing the best course of action depending on the patient’s genetic and clinical characteristics, AI can also assist in the treatment of illnesses. AI is also used in drug research and discovery, target identification, clinical trial design, and predicting the safety and effectiveness of novel medications. Still, there are ethical issues including data protection and the requirement for reliable data management systems. AI adoption in the healthcare sector is expensive, involving staff and facility investments as well as training healthcare professionals on its application.