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MXene-Based Intelligent Bioelectronic Interfaces: Emerging Platforms for Sensing, Energy Storage, and Therapeutic Applications
No full textMXene nanomaterials have emerged as highly versatile two-dimensional materials, characterized by exceptional electrical conductivity,
hydrophilicity, and easily modifiable surface chemistry. These attributes position MXenes as key materials in advancing
intelligent bioelectronic interfaces. This review explores the synthesis techniques, structural features, and physicochemical characteristics
of MXenes, highlighting their applicability across a range of fields. In the context of biosensing, MXenes’ large surface
area and efficient charge transport enable precise and selective detection of biological molecules. In energy storage, devices incorporating
MXenes such as flexible supercapacitors and microbatteries, demonstrate strong potential to fulfill the energy requirements
of wearable and implantable bioelectronic systems. Additionally, MXenes provide biocompatible platforms in therapeutic
biointerfaces that facilitate cellular stimulation and promote tissue repair. The advancement of intelligent, multifunctional
MXene-based platforms supports their smooth integration with biological systems, enabling real-time sensing and responsive
interventions. Despite these promising developments, challenges related to durability, scalable production, and maintaining
biocompatibility pose barriers to clinical adoption. This review seeks to offer a thorough overview of the current advancements
and future prospects of MXene-based bioelectronic interfaces
Evrensel Tasarımda Çocuk, Kent ve Mekan Üzerine
No full textAna makaleÇocuklar, mekânlar ve mimarlık arasındaki ilişkiyi ele alan çalışmalar, hem akademik dünyada hem de toplumsal alanda giderek daha fazla önem kazanmaktadır. Bu bağlamda, “Evrensel Tasarımda Çocuk, Kent ve Mekân” temasıyla 21-22 Kasım 2025 tarihinde Konya’da gerçekleştirilen 6. Ulusal Engellileştirilenler Sempozyumu, çocuklar, mekân, erişilebilirlik ve kapsayıcılık gibi konuları odağına alarak hem bilimsel hem de toplumsal sorunlar dikkat çeken önemli bir platform sağlamıştır. Bu sempozyumun, hem akademik hem de kişisel gelişimim açısından ne denli önemli olduğunu, doğduğum kadim şehir Konya’ya bilimsel bir katkı sunmanın yanı sıra, içimde taşıdığım vefa duygusunu ifade etme fırsatı olarak değerlendiriyorum. Konya, sadece çocukluğumun geçtiği sokaklar ve mahalleler ile değil, aynı zamanda mesleki bakış açılarımın şekillenmeye başladığı yer olarak hayatımda ayrıcalıklı bir yere sahiptir. Bunun yanı sıra, Selçuklu ve Osmanlı eserleriyle bezenmiş derin bir kültürel miras barındırması ve medeniyetler tarihine tanıklık eden kimliği sayesinde benim için çok daha anlamlı hale gelmiştir. Bu köklü tarihe sahip şehirde oluşturduğum mekân algısının, mesleki ilgi alanlarımı şekillendiren temel bir yapı taşı olduğuna inanıyorum
Wearable Electronic Patch for Physicochemical Data Transmission: Mxene-Based MEMS/NEMS Biosensors
No full textWearable electronic patches have attracted significant attention as platforms for continuous, non-invasive monitoring of physiological and physicochemical signals at the skin interface. Recent literature highlights MXene materials as particularly promising candidates for wearable biosensing applications due to their high electrical conductivity, tunable surface chemistry, mechanical flexibility, and favorable biocompatibility. When combined with micro- and nano-engineering strategies, MXene-based sensing elements can be integrated into compact and multifunctional MEMS/NEMS architectures, enabling smart patches with enhanced sensitivity, signal stability, and mechanical durability. On-skin sensing technologies reported in previous studies enable reliable acquisition of biopotential, biomechanical, and chemical biomarkers, supporting real-time health monitoring across diverse physiological conditions. Furthermore, the integration of these smart patches into emerging digital health ecosystems facilitates wireless data transmission to mobile devices, cloud-based platforms, and clinical networks, promoting continuous and connected healthcare monitoring. This review summarizes recent advances in MXene-based MEMS/NEMS wearable biosensors, with particular emphasis on material–device integration strategies, sensing mechanisms, detected biomarker classes, and system-level connectivity. In addition, current challenges, including long-term biocompatibility, scalable manufacturing, material stability under physiological conditions, and secure data management, are critically discussed. Addressing these limitations will be essential for the translation of MXene-enabled wearable technologies toward personalized, predictive, and adaptive healthcare applications
Design of Hyaluronic Acid-Based Glutathione-Gelatin-Collagen-Laminin Loaded 3D Bioactive Transdermal Tissue Scaffold: Determination of Biocompatibility, Antimicrobial and ROS
No full textThis study reports the development of a 3D-bioprinted hyaluronic acid (HyA)–based transdermal scaffold incorporating glutathione (GSH), gelatin, collagen (COL), and laminin to enhance wound healing through combined antioxidant, antibacterial, and regenerative functionalities. The scaffold was fabricated using a multi-layer bioprinting strategy, yielding a structurally stable matrix with tunable mechanical performance across Control, TDM_1, TDM_2, and TDM_3 formulations. Mechanical characterization revealed a progressive increase in stiffness with higher matrix density, with elastic modulus values ranging from 9.8 MPa (Control) to 14.3 MPa (TDM_3), and corresponding maximum stress values increasing from 1.32 to 2.19 MPa. Texture Profile Analysis (TPA) further demonstrated formulation-dependent improvements in functional mechanical behavior: hardness increased from 0.42 to 0.79 N, compressibility from 0.35 to 0.70 N, and adhesiveness from −0.18 to −0.37 N s across the scaffold series. These results indicate that optimized structures possess superior deformation resistance, energy absorption capacity, and tissue-adhesive characteristics required for transdermal stability. The GSH encapsulation efficiency reached 81.71%, with a loading capacity of 13.62%, and Franz diffusion studies revealed a sustained drug release of 63.17% over 24 h. Dynamic vapor sorption (DVS) showed 90% viability, and reactive oxygen species (ROS) levels were reduced by up to 45%, confirming biocompatibility and antioxidant efficacy. Antibacterial tests showed activity against E. coli and S. aureus at concentrations >200 μg/mL. SEM imaging revealed a porous microarchitecture supportive of fibroblast attachment and proliferation. Collectively, the updated mechanical and TPA findings confirm that the optimized bioprinted scaffold, particularly TDM_3, offers robust structural integrity, strong bioadhesive performance, sustained drug delivery, and enhanced bioactivity, positioning it as a promising multifunctional platform for chronic wound management
Wearable Technology for 2D MXene Based Supercapacitors
No full textThe growing demand for wearable electronics has intensified the need for lightweight, flexible, and highperformance
energy storage systems. MXene-based supercapacitors have emerged as a promising solution due
to their high electrical conductivity, large surface area, mechanical flexibility, and excellent electrochemical
performance. These features enable rapid charge–discharge capability, long cycling stability, and seamless
integration with flexible and stretchable substrates. This study reviews the application potential of MXene-based
supercapacitors in wearable technologies such as health monitoring systems, fitness trackers, smart textiles, and
AR/VR devices. In addition, key challenges, including large-scale production, oxidation stability, electrolyte
compatibility, and mechanical durability, are discussed. Recent strategies to enhance material stability and
device performance through surface modification and hybrid configurations are highlighted. MXene-based
supercapacitors are expected to play a crucial role in the development of next-generation self-powered and
smart wearable systems
Predictive Analytics for Hydrogen–Honge Oil Dual Fuel Engine Using Machine Learning
No full textPlant based fuels have been incessantly researched as a substitute of diesel. However, their use in compression
ignition engine tends to deteriorate engine performance and increase the engine emissions. Thereby, offsetting
the advantage of the fuel being carbon neutral. Hydrogen induction in intake manifold with the direct injection
of biofuel enhances the engine performance and simultaneously reduce the emissions. This way diesel can be
completely substituted with carbon neutral fuels. In the present study to replace diesel, honge oil was transesterified
to its methyl ester and used as the direct injected fuel and the flow rate of hydrogen was varied. The
results indicate an improvement in engine performance with higher in-cylinder pressure and heat release rate
with 30 L per minute (lpm) flow rate of hydrogen. The brake specific energy consumption (BSEC) of the engine
was reduced to 12.2kJ/kWh with the highest flow rate of hydrogen at full load condition. The unburnt hydrocarbon
emission, carbon monoxide emission and smoke opacity reduced from 30 ppm, 0.8% & 59% to 19 ppm,
0.48% & 47%, respectively with maximum flow rate of hydrogen. However, due to improvement in combustion,
the oxides of nitrogen emission increased from 1224 ppm to 1450 ppm with hydrogen premixing. For the same
engine, if fuel is varied then extensive experimental study is required for analysing the engine performance
which is costly, time consuming and may itself be a source of pollution. If the exhaust emissions can be accurately
predicted with variation in fuels, then the previously mentioned issues can be resolved. In this regard novel
features namely percentage of carbon, hydrogen and oxygen present in the fuel were used along with hydrogen
flow rate. Various algorithms were compared for predicting the emissions. The results show that the lowest mean
absolute percentage error (MAPE) and root mean square error (RMSE) of 0.78, 0.00647, 0.074, 0.00391, 0.155
and 0.013 was observed with support vector regression (SVR) for CO, HC and smoke emissions, respectively.
While gradient process regression (GPR) algorithm resulted in lowest error of MAPE (0.22) and RMSE (0.65) for
NO emission
Multi-Model Fusion Methods with Strong Generalization Capability for Online SOH Estimation of Lithium-Ion Batteries
No full textLithium-ion cells are essential in daily life, but accurately assessing their state of health (SOH) is challenging
because deep learning models need large, consistent cycling datasets that are often impractical to obtain.
Moreover, the real-world data can be incomplete or inconsistent. In this study, the SOH was predicted online
utilizing fusion models of convolutional neural network (CNN), long short-term memory (LSTM) convolution
block attention mechanism (CBAM), and multi-head attention mechanism (MHA) based architecture. Datasets
prepared by the reaserchers at Massachussets Institute of Technology (MIT), and Sandia National Laboratory
(SNL) were combined for training various models. Bayesian optimization algorithm was used to optimize the
hyperparameters. The results reveal that the CNN-LSTM fusion model enhances the accuracy of capacity estimation.
Oxford University dataset was used to test the efficacy of the models. Highest accuracy in prediction was
found with the CNN-LSTM model having lowest root mean squared error (RMSE) of 0.0136. Ablation experiments
were carried out and the performance of the fusion models was found better than the base models. The
trained models were also retrained for another voltage range. CALCE dataset, provided by the University of
Maryland, was utilized for the experiments. 2 and 7 cells dataset were separately used for the training. The CNNCBAM
framework with 5 CNN layers was found to be the best model. The models generated in this study can be
used for predicting the SOH online for cells having different kind of form, chemistry, charged/discharged at
different rates and varying temperatures demonstrating its practicality and generalization ability
A Cybersecurity Method to Detect SQL Injection Attacks Using Heuristic‑Driven Feature Selection and Machine Learning Algorithms
No full textSQL injection is a serious security risk that allows attackers to access application databases. SQL injection attacks can be identified using various methods, including machine learning algorithms. Finding the top-performing features in the training dataset is a combinatorial optimization problem known to be NP-complete. Finding the dataset’s most effective and significant features is the goal of feature selection. This study aims to optimize the sensitivity, specificity, and accuracy of the SQL injection detection method. The first stage of the suggested method involved creating a unique training dataset with 13 characteristics. A binary form of the Whale Optimization Algorithm was suggested to find the most effective features in the dataset. An effective SQL injection detection system was developed by combining the whale algorithm as a feature selector with various machine learning techniques. The suggested SQL injection detector achieved 98.88% accuracy, 99.35% sensitivity, and a 98.83% F1-score using an artificial neural network and the whale optimizer. Using the proposed strategy to select about 31% of the features improved the performance of the attack detectors
A New Hybrid Sensor Design Based on a Patch Antenna with an Enhanced Sensitivity Using Frequency-Selective Surfaces (FSS) in the Microwave Region for Non-Invasive Glucose Concentration Level Monitoring
No full textIn this study, a hybrid sensor based on a defective square-truncated patch antenna (STPA)
and a frequency-selective surface (FSS) was analyzed numerically and experimentally for
different glucose–distilled water solutions. Here, an FSS was employed to enhance the
sensitivity of the hybrid sensor. The sensing principle relies on monitoring variations in the
loss tangent (tanδ) and relative permittivity (εr) caused by different glucose concentrations
applied to the sample under test (SUT). An open-ended coaxial probe was used to measure
the complex permittivity of the solutions, which was then fitted to the Debye relaxation
model. The simulated and experimental results of the novel sensor showed good agreement
in a glucose concentration monitoring application. The sensor spanned the glucose range
from 0 mg/dL to 5000 mg/dL, exhibiting a sensitivity of 55.44 kHz/mgdL−1 and a figure
of merit (FOM) of 6.23 × 10−4 (1/mgdL−1) in the experiments and 53.60 kHz/mgdL−1 and
1.71 × 10−4 (1/mgdL−1) FOM in the simulations. When solutions with different concentrations
were tested in the SUT, the resonance frequency of the antenna ( f0, in GHz)
changed. To further characterize the sensor response, the relationship between the glucose
concentration (C, in mg/dL) and f0 was examined. A regression-based prediction model
was constructed to map the measured scattering parameters to the glucose concentration,
yielding a coefficient of determination (R2) of 0.976. The high sensitivity, compact size,
and compatibility with planar fabrication suggest that the proposed hybrid sensor has the
potential to contribute to the development of non-invasive glucose-monitoring systems
AGFP: A Deep Attention-Guided Framework for DWT-Based Image Steganography
No full textThis study introduces a novel attention-guided Discrete Wavelet Transform (DWT)-based steganography framework, named Attention-Guided Feature Perturbation (AGFP), which integrates deep visual attention maps with transform-domain embedding to enhance imperceptibility, robustness, and steganalysis resistance. Unlike recent deep-learning-based steganographic systems such as iSCMIS, JARS-Net, and RMSteg, which achieve high visual fidelity but are susceptible to statistical detection, AGFP perturbs only those wavelet coefficients that are identified as perceptually and statistically stable by attention mechanisms extracted from pre-trained CNN models (VGG19, ResNet50, AlexNet, and GoogLeNet). The proposed method is evaluated on the USC-SIPI dataset and the BOSSBase 1.01 benchmark. Experimental results show that AGFP achieves PSNR values between 64.29 and 55.43 dB and SSIM scores between 0.9999 and 0.9989 across varying payloads, indicating consistently high visual quality. While iSCMIS reports slightly higher PSNR and SSIM values, AGFP significantly outperforms all compared methods in bit error rate (BER)—achieving 0.01–0.12, compared to 0.45–0.47 for iSCMIS, 0.31–0.37 for RMSteg, and 0.57–0.75 for JARS-Net. Furthermore, AGFP attains the lowest RS, SPA, and SRM steganalysis detection scores among both classical and deep-learning-based systems. These results confirm that AGFP offers a more balanced and secure steganographic solution, combining high imperceptibility with substantially enhanced robustness and detectability resistance, positioning it as a strong alternative to recent deep-learning-based steganographic frameworks