Defects In Region Research Articles

Enhancing automatic inspection and characterization of carbon fiber composites through hyperspectral diffuse reflection analysis and k-means clustering

Numerous industries utilize carbon fiber composites (CFC) for their exceptional strength-to-weight ratio and stiffness. However, inherent manufacturing defects such as voids and delamination can undermine the material’s structural integrity and performance. This study introduces an advanced imaging technique employing hyperspectral imaging (HSI) to effectively detect and characterize flaws within CFC materials. HSI provides high-resolution spectral data, enabling precise analysis of material properties. Initial observations indicate a distinctive peak variability in dispersed reflectance spanning 440 nm to 600 nm for both pristine surfaces and defective regions of CFC sheets. The proposed methodology entails the automated characterization of CFC through a combination of HSI and an advanced clustering technique, k-means clustering (k-mc). The application of k-mc facilitates rapid and accurate categorization of defect locations. Statistical analysis reveals mean and standard deviation (SD) values of 0.34 and 0.33, respectively, for normal CFC surfaces, compared to 0.43 and 0.29 for cracked CFC sections. These discernible variations enable precise differentiation between defect-free and defective CFC specimens. By employing spectral signatures at 445 nm, 546 nm, and 585 nm as optical markers, the study accurately measures fracture penetration depths. Implementation of this approach generates 3D-resolved images, offering a comprehensive visualization of CFC imperfections. The proposed methodology presents a more automated and objective strategy for defect identification and categorization. This unique approach holds significant potential for industrial applications, particularly in scenarios necessitating efficient and precise evaluation of numerous CFC components.

Damage imaging in plates by evaluating local entropy in guided wavefield data

This paper presents a new algorithm called broadband entropy-based two-stage damage imaging (BE2DI) for the detection and imaging of defects in multi-layer plates.The method differentiates defects from either local scattering phenomena of propagating Lamb waves or locally increased vibrational activity for stimulated global plate resonances by exploiting the higher vibrational activity of full-field broadband vibration signals at defective regions and the distinct spatial behavior of defects and artifacts. Using these features, first, a candidate damage map is constructed by means of iterative thresholding based on descriptive statistics of broadband data. Afterwards, an adaptive regional damage index based on local entropy is introduced, which utilizes operational deflection shapes to process previously identified candidate regions and subsequently reveal both shallow and deep defects. BE2DI can be seen as an automated local defect resonance identification approach in the case of shallow defects. Multiple plates having various defect types with different sizes and depths are investigated. Broadband chirp signals are used to excite the plates using low-power piezoelectric actuators, and vibrational data is registered by 3D scanning laser Doppler vibrometry. The obtained results quantitatively and qualitatively demonstrate that the proposed method can effectively detect different kinds of damage, regardless of being shallow or deep.

Moiré effect enables versatile design of topological defects in nematic liquid crystals

Recent advances in surface-patterning techniques of liquid crystals have enabled the precise creation of topological defects, which promise a variety of emergent applications. However, the manipulation and application of these defects remain limited. Here, we harness the moiré effect to engineer topological defects in patterned nematic liquid crystal cells. Specifically, we combine simulation and experiment to examine a nematic cell confined between two substrates of periodic surface anchoring patterns; by rotating one surface against the other, we observe a rich variety of highly tunable, novel topological defects. These defects are shown to guide the three-dimensional self-assembly of colloids, which can conversely impact defects by preventing the self-annihilation of loop-defects through jamming. Finally, we demonstrate that certain nematic moiré cells can engender arbitrary shapes represented by defect regions. As such, the proposed simple twist method enables the design and tuning of mesoscopic structures in liquid crystals, facilitating applications including defect-directed self-assembly, material transport, micro-reactors, photonic devices, and anti-counterfeiting materials.

Output breakdown characteristics of amorphous InGaZnO thin-film transistors at high gate voltage

Output characteristics of top-gate amorphous InGaZnO thin-film transistors are investigated at high gate voltage. With the increasing drain voltage, an output breakdown crops up closely following a drastic uprush of drain current. Such dramatically elevated drain current derives from the self-heating (SH) effect-generated channel donors. Measured at different high gate voltages, the output breakdown occurs in either linear or saturation regime, respectively, corresponding to normally-on and normally-off transfer curve, while the SH-triggering powers are almost identical. The conductor-like channel originates from the SH-induced high donor population, while the disconnection between channel and drain is caused by a highly defective region near drain, where the hot-carrier damage is violently activated by the SH-induced high temperature and the high drain electric field in saturation regime.

Janus Membrane with Intrafibrillarly Strontium-Apatite-Mineralized Collagen for Guided Bone Regeneration.

Commercial collagen membranes face difficulty in guided bone regeneration (GBR) due to the absence of hierarchical structural design, effective interface management, and diverse bioactivity. Herein, a Janus membrane called SrJM is developed that consists of a porous collagen face to enhance osteogenic function and a dense face to maintain barrier function. Specifically, biomimetic intrafibrillar mineralization of collagen with strontium apatite is realized by liquid precursors of amorphous strontium phosphate. Polycaprolactone methacryloyl is further integrated on one side of the collagen as a dense face, which endows SrJM with mechanical support and a prolonged lifespan. In vitro experiments demonstrate that the dense face of SrJM acts as a strong barrier against fibroblasts, while the porous face significantly promotes cell adhesion and osteogenic differentiation through activation of calcium-sensitive receptor/integrin/Wnt signaling pathways. Meanwhile, SrJM effectively enhances osteogenesis and angiogenesis by recruiting stem cells and modulating osteoimmune response, thus creating an ideal microenvironment for bone regeneration. In vivo studies verify that the bone defect region guided by SrJM is completely repaired by newly formed vascularized bone. Overall, the outstanding performance of SrJM supports its ongoing development as a multifunctional GBR membrane, and this study provides a versatile strategy of fabricating collagen-based biomaterials for hard tissue regeneration.

A rail fastener defect detection algorithm based on improved YOLOv5

Being a crucial component of railway tracks, monitoring the health condition of fasteners stands as a critical aspect within the realm of railroad track management, ensuring the normal passage of trains. However, traditional track fastener detection methods mainly use artificial checks, giving rise to challenges encompassing reduced efficiency, safety hazards, and poor detection accuracy. Consequently, we introduce an innovative model for the detection of track fastener defects, termed YOLOv5-CGBD. In this study, we first imbue the backbone network with the CBAM attention mechanism, which elevates the network’s emphasis on pertinent feature extraction within defective regions. Subsequently, we replace the standard convolutional blocks in the neck network with the GSConv convolutional module, achieving a delicate balance between the model’s accuracy and computational speed. Augmenting our model’s capacities for efficient feature map fusion and reorganization across diverse scales, we integrate the weighted bidirectional feature pyramid network (BiFPN). Ultimately, we manipulate a lightweight decoupled head structure, which improves both detection precision and model robustness. Concurrently, to enhance the model’s performance, a data augmentation strategy is employed. The experimental findings testify to the YOLOv5-CGBD model’s ability to conduct real-time detection, with mAP0.5 scores of 0.971 and 0.747 for mAP0.5:0.95, surpassing those of the original YOLOv5 model by 2.2% and 4.1%, respectively. Furthermore, we undertake a comparative assessment, contrasting the proposed methodology with alternative approaches. The experimental outcomes manifest that the YOLOv5-CGBD model exhibits the most exceptional comprehensive detection performance while concurrently maintaining a high processing speed.

Study on Synergistic Effects of Nanohydroxyapatite/High-Viscosity Carboxymethyl Cellulose Scaffolds Stimulated by LIPUS for Bone Defect Repair of Rats.

Despite the self-healing capacity of bone, the regeneration of critical-size bone defects remains a major clinical challenge. In this study, nanohydroxyapatite (nHAP)/high-viscosity carboxymethyl cellulose (hvCMC, 6500 mPa·s) scaffolds and low-intensity pulsed ultrasound (HA-LIPUS) were employed to repair bone defects. First, hvCMC was prepared from ramie fiber, and the degree of substitution (DS), purity, and content of NaCl of hvCMC samples were 0.91, 99.93, and 0.017%, respectively. Besides, toxic metal contents were below the permissible limits for pharmaceutically used materials. Our results demonstrated that the hvCMC is suitable for pharmaceutical use. Second, nHAP and hvCMC were employed to prepare scaffolds by freeze-drying. The results indicated that the scaffolds were porous, and the porosity was 35.63 ± 3.52%. Subsequently, the rats were divided into four groups (n = 8) randomly: normal control (NC), bone defect (BD), bone defect treated with nHAP/hvCMC scaffolds (HA), and bone defect treated with nHAP/hvCMC scaffolds and stimulated by LIPUS (HA-LIPUS). After drilling surgery, nHAP/hvCMC scaffolds were implanted in the defect region of HA and HA-LIPUS rats. Meanwhile, HA-LIPUS rats were treated by LIPUS (1.5 MHz, 80 mW cm-2) irradiation for 2 weeks. Compared with BD rats, the maximum load and bone mineral density of HA-LIPUS rats were increased by 20.85 and 51.97%, respectively. The gene and protein results indicated that nHAP/hvCMC scaffolds and LIPUS promoted the bone defect repair and regeneration of rats significantly by activating Wnt/β-catenin and inhibiting OPG/RANKL signaling pathways. Overall, compared with BD rats, nHAP/hvCMC scaffolds and LIPUS promoted bone defect repair significantly. Furthermore, the research results also indicated that there are synergistic effects for bone defect repair between the nHAP/hvCMC scaffolds and LIPUS.

Osteoimmunomodulatory bioinks for 3D bioprinting achieve complete regeneration of critical-sized bone defects

Regeneration the critical-sized bone defects remains a great challenge to clinical therapy due to the inflammatory microenvironment and lack of stem cells in the region of the bone defects. 3D bioprinted scaffolds based on bioactive ink and loaded active cells can promote the inflammatory microenvironment and cell viability, thereby enhancing bone regeneration. In this study, 10 % Gelatin-methacryloyl (GelMA)/5 %Sr substituted xonotlite (Sr-CSH) nanocomposite hydrogel was developed as a bioink to encapsulate bone marrow mesenchymal stem cells (BMSCs), and then constructed a biomimetic bone tissue by 3D bioprinting. The incorporation of Sr-CSH nanowires enhanced the printing accuracy and mechanical property of GelMA, and enhanced the osteogenic differentiation of BMSCs. In addition, Sr-CSH induced macrophage M2 polarization, which modulated the inflammatory microenvironment and further promoted osteogenic differentiation of BMSCs. In rat critical-sized calvarial defects model, 3D bioprinted scaffolds based on GelMA-Sr-CSH bioinks laden with BMSCs achieve complete bone repair. In summary, this study developed an osteoimmunomodulatory bioink, and 3D bioprinted scaffolds laden with stem cells may be an effective method for achieving complete regeneration of critical-sized bone defects.

Casting defect region segmentation method based on dual-channel encoding–fusion decoding network

Segmenting casting defect regions is vital for assessing defect levels in casting products. In complex backgrounds with multi-scale defects and regions of fuzzy and weak texture, existing methods often fail to capture detailed features of the defect, leading to incomplete segmentation. This study developed a casting defect region segmentation method based on a dual–channel encoding–fusion decoding (DCE–FD) network. Initially, an encoding network module based on a deep and shallow dual–channel structure (ENM–DSDCS) was established to extract macroscopic and multi-scale detail features using deep and shallow structure networks, respectively. This approach ensures comprehensive feature extraction from multi-scale fuzzy and weak texture defect areas. Further, a decoding network module based on attention-based bidirectional guidance fusion (DNM–ABGF) was developed. This module guides the semantic and multi-scale detail branch features to achieve complementary information fusion at each scale level during the decoding stage, thereby preserving fuzzy boundaries and other details in the fusion process and enhancing the accuracy and integrity of segmentation. Experimental results demonstrate that the mean intersection over union (mIOU) and Dice coefficients for defect region segmentation in radiographic images of castings were 92% and 75.71%, respectively. These metrics surpass those of eight advanced segmentation methods in terms of accuracy and consistency. The proposed method significantly improves sensitivity to feature detail and accurately segments potential fuzzy regions of multi-scale defects, offering promising advancements in the nondestructive testing of casting products.

Achieving the ideal balance between biological and mechanical requirements in composite bone scaffolds through a voxel-based approach

Achieving successful bone regeneration necessitates the design of scaffolds that meet diverse biological and mechanical requirements, often leading to conflicts in the design parameters. A key conflict arises between scaffold porosity and stiffness. Increasing porosity facilitates cell infiltration and nutrient exchange, promoting bone regeneration. However, higher porosity compromises scaffold stiffness, which is crucial for providing structural support in the defective region. Furthermore, appropriate scaffold stiffness is crucial for preventing stress shielding. Conventional geometry-based design methods utilizing single-phase materials have limited flexibility in resolving such conflicts. To address this challenge, we propose a voxel-based method for designing composite scaffolds composed of hydroxyapatite (HA) and polylactic acid (PLA). Our strategy involves first satisfying primary biological requirements by selecting appropriate porosity, pore shape, and size. Subsequently, scaffold stiffness requirements are met by selecting suitable phase materials and tuning their contents. The study demonstrates that the voxel-based approach effectively balances both biological and mechanical requirements in scaffold design. This method addresses the limitations of traditional designs by achieving an optimal balance between porosity and stiffness, which is crucial for scaffold performance in biomedical applications. Moreover, the scaffolds designed using this method can be manufactured using voxel-based 3D printing technology, which is emerging in the field. Future advancements in voxel-based 3D printing technology will further enhance the feasibility and practicality of this approach for bone tissue engineering applications.

Piezo-enhanced near infrared photocatalytic nanoheterojunction integrated injectable biopolymer hydrogel for anti-osteosarcoma and osteogenesis combination therapy

Preventing local tumor recurrence while promoting bone tissue regeneration is an urgent need for osteosarcoma treatment. However, the therapeutic efficacy of traditional photosensitizers is limited, and they lack the ability to regenerate bone. Here, a piezo-photo nanoheterostructure is developed based on ultrasmall bismuth/strontium titanate nanocubes (denoted as Bi/SrTiO3), which achieve piezoelectric field-driven fast charge separation coupling with surface plasmon resonance to efficiently generate reactive oxygen species. These hybrid nanotherapeutics are integrated into injectable biopolymer hydrogels, which exhibit outstanding anticancer effects under the combined irradiation of NIR and ultrasound. In vivo studies using patient-derived xenograft models and tibial osteosarcoma models demonstrate that the hydrogels achieve tumor suppression with efficacy rates of 98.6 % and 67.6 % in the respective models. Furthermore, the hydrogel had good filling and retention capabilities in the bone defect region, which exerted bone repair therapeutic efficacy by polarizing and conveying electrical stimuli to the cells under mild ultrasound radiation. This study provides a comprehensive and clinically feasible strategy for the overall treatment and tissue regeneration of osteosarcoma.

Template-based Feature Aggregation Network for industrial anomaly detection

Industrial anomaly detection plays a crucial role in ensuring product quality control. Therefore, proposing an effective anomaly detection model is of great significance. While existing feature-reconstruction methods have demonstrated excellent performance, they face challenges with shortcut learning, which can lead to undesirable reconstruction of anomalous features. To address this concern, we present a novel feature-reconstruction model called the Template-based Feature Aggregation Network (TFA-Net) for anomaly detection via template-based feature aggregation. Specifically, TFA-Net first extracts multiple hierarchical features from a pre-trained convolutional neural network for a fixed template image and an input image. Instead of directly reconstructing input features, TFA-Net aggregates them onto the template features, effectively filtering out anomalous features that exhibit low similarity to normal template features. Next, TFA-Net utilizes the template features that have already fused normal features in the input features to refine feature details and obtain the reconstructed feature map. Finally, the defective regions can be located by comparing the differences between the input and reconstructed features. Our template-based feature aggregation schema yields a nontrivial and meaningful feature reconstruction task. The simple, yet efficient, TFA-Net exhibits state-of-the-art detection performance on various real-world industrial datasets. Additionally, it fulfills the real-time demands of industrial scenarios, rendering it highly suitable for practical applications in the industry.

Tensile failure mechanisms investigation of mesophase pitch-based carbon fibers based on continuous defective graphene nanoribbon model

Mesophase pitch (MPP)-based carbon fibers exhibit outstanding mechanical properties, notably an exceptionally high Young’s modulus. Despite extensive investigations into the microstructure of MPP-based carbon fibers, the influence of these factors on deformation mechanisms under tension remains unclear. This study employs the continuous defective graphene nanoribbons (dGNR) atomistic structure model for molecular dynamics simulations to explore the tensile failure mechanisms of MPP-based carbon fibers. In the simulation model, the structure of the defective region was generated through high-temperature annealing, and a transition region was introduced to prevent distortion and damage to the active graphene edges. The simulation reveals the evolutionary process of the microstructure of MPP-based carbon fibers under tension and achieves Young’s modulus predictions with greater accuracy than theoretical models. Additionally, the study shows that different strengths of interactions between adjacent graphene nanoribbons can lead to two distinct failure modes. Models with larger crystallite dimensions along the fiber axis and lower average defective concentrations exhibit geometric deformation coordination between adjacent nanoribbons, potentially elucidating the increasing strength trend in MPP-based carbon fibers with rising graphitization levels. Our simulations provide insights into the tensile failure mechanisms of MPP-based carbon fibers, offering valuable guidance for regulating their microstructure to enhance mechanical performance.

Defect imaging and identification in asphalt materials using coplanar capacitance sensors with single-pair electrodes

Coplanar capacitance imaging technology (CCIT) is a non-destructive method with intuitive and accurate identification. This paper aims to realize defect identification in asphalt materials based on the CCIT using coplanar single-pair electrode capacitance sensor (CSCS). Firstly, the Linear Back Projection (LBP) algorithm, the Landweber algorithm, and the Kalman-Filter (KF) algorithm, are compared and evaluated. Then, the principal component analysis (PCA) method is utilized to fuse the reconstructed images. In addition, the OTSU method, the iterative threshold (IT) method, and the genetic algorithm (OA) method, is compared and utilized to quantify the defective region. Finally, this investigation analyzes the reconstructed and segmented images of defects in asphalt materials. It is concluded that the KL algorithm is the most suitable algorithm to reconstruct the defective images. The PCA method improve the quality of the reconstructed defective images. The defective region is determined by the OTSU method, which is the most approximate imaging segment method. It is found that the CCIT can detect the invisible defect depth in asphalt materials. The different defect mediums in various asphalt materials can be identified by the CCIT. The segmented defective region error in asphalt materials is less than 13%, demonstrating that the CCIT is effective for the identification of defect shape details in asphalt materials. The segmented imaging precision of square defects in asphalt materials is the highest; circular defects are the next lowest; and triangular defects are the lowest. The outcomes of this research can assist engineers in realizing intuitive and high-accuracy identification of various invisible defects in shallow asphalt layers in bridge deck asphalt pavement utilizing the CCIT.

Exosomes derived from mucoperiosteum Krt14+Ctsk+ cells promote bone regeneration by coupling enhanced osteogenesis and angiogenesis.

Repair of large bone defects is a sophisticated physiological process involving the meticulous orchestration of cell activation, proliferation, and differentiation. Cellular interactions between different cell types are paramount for successful bone regeneration, making it a challenging yet fascinating area of research and clinical practice. With increasing evidence underscoring the essential role of exosomes in facilitating intercellular and cell-microenvironment communication, they have emerged as an encouraging therapeutic strategy to promote bone repair due to their non-immunogenicity, diverse sources, and potent bioactivity. In this study, we characterized a distinctive population of Krt14+Ctsk+ cells from the orbital mucoperiosteum. In vitro experiments confirmed that exosomes from Krt14+Ctsk+ cells dramatically boosted the capacities of human umbilical vein endothelial cells (HUVECs) to proliferate, migrate, and induce angiogenesis. Additionally, the exosomes notably elevated the expression of osteogenic markers, thereby indicating their potential to augment osteogenic capabilities. Furthermore, in vivo experiments utilizing a rat calvarial defect model verified that exosome-loaded sodium alginate (SA) hydrogels accelerated local vascularized bone regeneration within the defective regions. Collectively, these findings suggest that exosomes secreted by Krt14+Ctsk+ cells offer an innovative method to accelerate bone repair via coupling enhanced osteogenesis and angiogenesis, highlighting the therapeutic potential in bone repair.

Transforming patterned defects into dynamic poly-regional topographies in liquid crystal oligomers.

We create high-aspect-ratio dynamic poly-regional surface topographies in a coating of a main-chain liquid crystal oligomer network (LCON). The topographies form at the topological defects in the director pattern organized in an array which are controlled by photopatterning of the alignment layer. The defect regions are activated by heat and/or light irradiation to form reversible topographic structures. Intrinsically, the LCON is rubbery and sensitive to temperature changes, resulting in shape transformations. We further advanced such system to make it light-responsive by incorporating azobenzene moieties. Actuation reduces the molecular order of the LCON coating that remains firmly adhered to the substrate which gives directional shear stresses around the topological defects. The stresses relax by deforming the surfaces by forming elevations or indents, depending on the type of defects. The formed topographies exhibit various features, including two types of protrusions, ridges and valleys. These poly-regional structures exhibit a large modulation amplitude of close to 60%, which is 6 times larger than the ones formed in liquid crystal networks (LCNs). After cooling or by blue light irradiation, the topographies are erased to the initial flat surface. A finite element method (FEM) model is adopted to simulate structures of surface topographies. These dynamic surface topographies with multilevel textures and large amplitude expand the application range, from haptics, controlled cell growth, to intelligent surfaces with adjustable adhesion and tribology.

Study of Various Modalities of Management of Lower One-third Leg Defects in a Tertiary Care Hospital

Background: Lower extremity reconstruction is essential to plastic surgery and focuses on treating wounds and defects secondary to trauma, cancer, or chronic disease processes. Anatomical features of the lower third of the leg like subcutaneous bone surrounded by tendons with no muscles and vessels in isolated compartments with little intercommunication between them make the coverage of the wounds in the region a challenging problem. Aim: to study the various modalities of surgical management of lower 1/3rd leg defects. The study also aims to study the defect size and region in planning various flaps possible in the reconstruction ladder. Methods: The study was conducted from September 2014 to June 2017 on patients admitted to the Department of Burns, Plastic & Reconstructive Surgery and referred patients from the Department of General Surgery & Orthopaedic Surgery, S C B Medical College & Hospital, Cuttack. The Study includes all lower 3rd Leg defect varieties for different surgical treatment modalities. Results: Various reconstructive methods were used to cover the lower 1/3rdleg defects; the maximum was the free muscle transfer in 9 cases. The following typical flap used was the reverse sural flap. Conclusion: The goal in lower limb reconstruction involves needing a stable skeleton, allowing weight-bearing status, with adequate soft tissue coverage to nourish and protect the underlying bone. What one would use for reconstruction depends upon the surgeon’s familiarity and comfort levels with the technique as much as the various circumstances.

Surface topology of MXene flakes induces the selection of the sintering mechanism for supported Pt nanoparticles.

Sintering of metal nanocatalysts leading to particle growth and subsequent performance deactivation is a primary issue that hinders their practical applications. While metal-support interaction (MSI) is considered as the critical factor which influences the sintering behavior, the underlying microscopic mechanism and kinetics remain incompletely understood. Here, by using in situ scanning transmission electron microscopy (STEM) and theoretical analysis, we reveal the selection rule of the sintering mechanism for Pt nanoparticles on a two-dimensional (2D) MXene (Ti3C2T x ) support, which relies on the surface topology of MXene flakes. It is demonstrated that the sintering of Pt nanoparticles proceeds via Ostwald ripening (OR) in the surface defect (such as steps and pore edges) regions of MXene flakes due to strong MSI on the Pt/MXene interface; conversely, weak MSI between Pt nanoparticles and the planar surface of MXene leads to prevalent particle migration and coalescence (PMC) for sintering. Furthermore, our quantitative analysis shows a significant divergence in sintering rates for PMC and OR processes. These microscopic observations suggest a clear "sintering mechanism-MSI" relationship for Pt/MXene nanocatalysts and may shed light on the design of novel nanocatalysts.

Boron-doped graphene topological defects: unveiling high sensitivity to NO molecule for gas sensing applications.

Global air quality has deteriorated significantly in recent years due to large emissions from the transformation industry and combustion vehicles. This issue requires the development of portable, highly sensitive, and selective gas sensors. Nanostructured materials, including defective graphene, have emerged as promising candidates for such applications. In this work, we investigated the B-doped topological line defect in graphene as a sensing material for various gas molecules (CO, CO2, NO, and NH3) based on a combination of density functional theory and the non-equilibrium Green's function method. The electronic transport calculations reveal that the electric current can be confined to the line defect region by gate voltage control, revealing highly reactive sites. The B-doped topological line defect is metallic, favoring the adsorption of NO and NH3 over CO and CO2 molecules. We notice changes in the conductance after gas molecule adsorption, producing a sensitivity of 50% (16%) for NO (NH3). In addition, the recovery time for nitride gases was calculated for different temperatures and radiation frequencies. At 300 K the ultraviolet (UV) has a fast recovery time compared to the visible (VIS) one by about two orders of magnitude. This study gives an understanding of how engineering transport properties at the microscopic level (by topological line defect and chemical B-doping) leads to promising nanosensors for detecting nitride gas.

Review of the 3D bioprinting methods and materials applicable in 4D bioprinting

The advent of 4D bioprinting, a technique that integrates time dimensions into the conventional 3D method, substantially changed tissue engineering. By providing a time dimension to conventional 3D printing, the innovative process of 4D bioprinting is revolutionising tissue engineering. This development allows the creation of dynamic tissues, allowing structures to change and adapt over time to reflect the complexity of natural tissues. Specifically, shape memory polymers enable customised scaffold fabrication with improved mechanical strength and biomimicry for use in bone tissue engineering. An essential additional use for soft tissue regeneration is the development of injectable thermosensitive hydrogels that react to body temperature. Within asymmetrical defect regions, these hydrogels gel, offering a flexible cure for healing injured soft tissues. In addition to structural considerations, the potential applications of 4D bioprinting include organisms, vascularisation and dynamic disease models. Despite the advancements, issues like suitable biocompatibility and long-term stability still need to be resolved. However, by creating complex, adaptable and patient-specific designs in the realm of tissue engineering, 4D bioprinting has enormous potential to transform personalised medicine and regenerative therapies. This review provides a comprehensive investigation into the future of regenerative medicine by exploring the various uses and potentially revolutionary effects of 4D bioprinting in tissue engineering.