Development of 3D printed enzymatic biofuel cells for powering implantable biomedical devices

The drive toward device miniaturisation in the field of enzyme-based bioelectronics established a need for multi-dimensional geometrically structured and highly effective microelectrodes, which are difficult to implement and manufacture in devices such as biofuel cells and sensors. Additive manufacturing coupled with electroless metal plating enables the production of three-dimensional (3D) conductive microarchitectures with high surface area for potential applications in such devices. However, interfacial delamination between the metal layer and the polymer structure is a major reliability concern, which results in device performance degradation and eventually device failure. This thesis demonstrates a method to produce a highly conductive and robust metal layer on a 3D printed polymer microstructure with strong adhesion by introducing an interfacial adhesion layer. Prior to 3D printing, multifunctional acrylate monomers with alkoxysilane (-Si-(OCH3)3) were synthesised via the Thiol-Michael addition reaction between pentaerythritol tetraacrylate (PETA) and 3-mercaptopropyltrimethoxysilane (MPTMS) with a 1:1 stoichiometric ratio. Alkoxysilane functionality remains intact during photopolymerisation in a projection micro-stereolithography (PµSLA) system and is utilised for the sol-gel reaction with MPTMS post-functionalisation of the 3D printed microstructure to build an interfacial adhesion layer. This functionalisation leads to the implementation of abundant thiol functional groups on the surface of the 3D printed microstructure, which can act as a strong binding site for gold during electroless plating to improve interfacial adhesion. The 3D conductive microelectrode prepared by this technique exhibited excellent conductivity of 2.2×107 S/m (53% of bulk gold) with strong adhesion between a gold layer and a polymer structure even after harsh sonication and adhesion tape test, which offers potential to build a robust 3D conductive microarchitecture for applications such as biosensors and biofuel cells. As a proof-of-concept, the microelectrode with gold-coated complex lattice geometry was employed as an enzymatic glucose anode, which showed a significant increase in the current output compared to the one in the simple cube form. As the first approach, glucose oxidase was used as an enzyme. To find the optimal protocol for the enzyme immobilisation, the enzyme was first immobilised on agarose to achieve the enzyme’s highest activity and stability. Then, this immobilisation protocol was applied to immobilise the enzyme on the gold electrode surface. Preliminary studies on the preparation of 3D gold diamond lattice microelectrode modified with cysteamine and glucose oxidase as a bioanode for single cell enzymatic biofuel cell (EFC) application were performed, which demonstrated high current density of 0.38 μA cm–2 at 0.35 V in glucose solutions. This method for fabrication of 3D conductive microelectrodes offers potential for several biological applications. Instead of using a thiol, the surface of the 3D-printed part can be functionalised with different other functional groups to create an appropriate surface for biomolecules and cell adhesion. Furthermore, the surface of thiol functionalised printed parts can be perfect for additional metal coatings, opening the door to the creation of highly efficient and customised implantable energy harvesters and biosensors

Development of 3D printed enzymatic biofuel cells for powering implantable biomedical devices

The drive toward device miniaturisation in the field of enzyme-based bioelectronics established a need for multi-dimensional geometrically structured and highly effective microelectrodes, which are difficult to implement and manufacture in devices such as biofuel cells and sensors. Additive manufacturing coupled with electroless metal plating enables the production of three-dimensional (3D) conductive microarchitectures with high surface area for potential applications in such devices. However, interfacial delamination between the metal layer and the polymer structure is a major reliability concern, which results in device performance degradation and eventually device failure. This thesis demonstrates a method to produce a highly conductive and robust metal layer on a 3D printed polymer microstructure with strong adhesion by introducing an interfacial adhesion layer. Prior to 3D printing, multifunctional acrylate monomers with alkoxysilane (-Si-(OCH3)3) were synthesised via the Thiol-Michael addition reaction between pentaerythritol tetraacrylate (PETA) and 3-mercaptopropyltrimethoxysilane (MPTMS) with a 1:1 stoichiometric ratio. Alkoxysilane functionality remains intact during photopolymerisation in a projection micro-stereolithography (PµSLA) system and is utilised for the sol-gel reaction with MPTMS post-functionalisation of the 3D printed microstructure to build an interfacial adhesion layer. This functionalisation leads to the implementation of abundant thiol functional groups on the surface of the 3D printed microstructure, which can act as a strong binding site for gold during electroless plating to improve interfacial adhesion. The 3D conductive microelectrode prepared by this technique exhibited excellent conductivity of 2.2×107 S/m (53% of bulk gold) with strong adhesion between a gold layer and a polymer structure even after harsh sonication and adhesion tape test, which offers potential to build a robust 3D conductive microarchitecture for applications such as biosensors and biofuel cells. As a proof-of-concept, the microelectrode with gold-coated complex lattice geometry was employed as an enzymatic glucose anode, which showed a significant increase in the current output compared to the one in the simple cube form. As the first approach, glucose oxidase was used as an enzyme. To find the optimal protocol for the enzyme immobilisation, the enzyme was first immobilised on agarose to achieve the enzyme’s highest activity and stability. Then, this immobilisation protocol was applied to immobilise the enzyme on the gold electrode surface. Preliminary studies on the preparation of 3D gold diamond lattice microelectrode modified with cysteamine and glucose oxidase as a bioanode for single cell enzymatic biofuel cell (EFC) application were performed, which demonstrated high current density of 0.38 μA cm–2 at 0.35 V in glucose solutions. This method for fabrication of 3D conductive microelectrodes offers potential for several biological applications. Instead of using a thiol, the surface of the 3D-printed part can be functionalised with different other functional groups to create an appropriate surface for biomolecules and cell adhesion. Furthermore, the surface of thiol functionalised printed parts can be perfect for additional metal coatings, opening the door to the creation of highly efficient and customised implantable energy harvesters and biosensors

Study of nonlinear dynamics of a particle on a rotating parabola by using efficient mathematical techniques

In this paper, nonlinear dynamics of a particle on a rotating parabola arising in the mechanical engineering is proposed. Two efficient mathematical techniques called variational iteration method and variational approach are used to solve the governing differential equation of motion. To assess the accuracy of solutions, we compare the results with the Runge-Kutta 4th order. An excellent agreement of solutions with the numerical results and published results has been demonstrated. The results reveal that the present methods are very effective and convenient in predicting the solution of such problems and find a wide application in new engineering problems

Active Reinforcement Learning for the Semantic Segmentation of Images Captured by Mobile Sensors

Neural Networks have been employed to attain acceptable performance on semantic segmentation. To perform well, many supervised learning algorithms require a large amount of annotated data. Furthermore, real-world datasets are frequently severely unbalanced, resulting in poor detection of underrepresented classes. The annotation task requires time-consuming human labor. This thesis investigates the use of a reinforced active learning as region selection method to reduce human labor while achieving competitive results. A Deep Query Network (DQN) is utilized to identify the best strategy to label the most informative regions of the image. A Mean Intersection over Union (MIoU) training performance equivalent to 98% of the fully supervised segmentation network was achieved with labeling only 8% of dataset. Another 8% of labelled dataset was used for training the DQN. The performance of all three segmentation networks trained with regions selected by Frequency Weighted Average (FWA) IoU is better in comparison with baseline methods

Deep Learning meets Blockchain for Automated and Secure Access Control

Access control is a critical component of computer security, governing access to system resources. However, designing policies and roles in traditional access control can be challenging and difficult to maintain in dynamic and complex systems, which is particularly problematic for organizations with numerous resources. Furthermore, traditional methods suffer from issues such as third-party involvement, inefficiency, and privacy gaps, making transparent and dynamic access control an ongoing research problem. Moreover detecting malicious activities and identifying users who are not behaving appropriately can present notable difficulties. To address these challenges, we propose DLACB, a Deep Learning Based Access Control Using Blockchain, as a solution to decentralized access control. DLACB uses blockchain to provide transparency, traceability, and reliability in various domains such as medicine, finance, and government while taking advantage of deep learning to not rely on predefined policies and eventually automate access control. With the integration of blockchain and deep learning for access control, DLACB can provide a general framework applicable to various domains, enabling transparent and reliable logging of all transactions. As all data is recorded on the blockchain, we have the capability to identify malicious activities. We store a list of malicious activities in the storage system and employ a verification algorithm to cross-reference it with the blockchain. We conduct measurements and comparisons of the smart contract processing time for the deployed access control system in contrast to traditional access control methods, determining the time overhead involved. The processing time of DLBAC demonstrates remarkable stability when exposed to increased request volumes.Comment: arXiv admin note: text overlap with arXiv:2303.1475

Periodic solution to a nonlinear oscillator arising in micro electro mechanical system

In this paper, a periodic solution of nonlinear oscillator arising in the micro-beam based microelectromechanical system (MEMS) has been analytically achieved. The Amplitude Frequency Formulation (AFF) and Max Min Approach (MMA) have been used. In the second method (which is called MMA), an approximate solution of the nonlinear equation can be easily deduced by finding Maximal and minimal solution thresholds of this nonlinear problem. What we understood is that both methods, works properly and scales down the deal of the work. Compare conclusions with the results from fourth order Runge-Kutta method and energy balance method (EBM) shows that obtained results are of high accuracy and convenient

Multi-scale convolutional neural network for automated AMD classification using retinal OCT images

BACKGROUND AND OBJECTIVE: Age-related macular degeneration (AMD) is the most common cause of blindness in developed countries, especially in people over 60 years of age. The workload of specialists and the healthcare system in this field has increased in recent years mainly due to three reasons: 1) increased use of retinal optical coherence tomography (OCT) imaging technique, 2) prevalence of population aging worldwide, and 3) chronic nature of AMD. Recent advancements in the field of deep learning have provided a unique opportunity for the development of fully automated diagnosis frameworks. Considering the presence of AMD-related retinal pathologies in varying sizes in OCT images, our objective was to propose a multi-scale convolutional neural network (CNN) that can capture inter-scale variations and improve performance using a feature fusion strategy across convolutional blocks. METHODS: Our proposed method introduces a multi-scale CNN based on the feature pyramid network (FPN) structure. This method is used for the reliable diagnosis of normal and two common clinical characteristics of dry and wet AMD, namely drusen and choroidal neovascularization (CNV). The proposed method is evaluated on the national dataset gathered at Hospital (NEH) for this study, consisting of 12649 retinal OCT images from 441 patients, and the UCSD public dataset, consisting of 108312 OCT images from 4686 patients. RESULTS: Experimental results show the superior performance of our proposed multi-scale structure over several well-known OCT classification frameworks. This feature combination strategy has proved to be effective on all tested backbone models, with improvements ranging from 0.4% to 3.3%. In addition, gradual learning has proved to be effective in improving performance in two consecutive stages. In the first stage, the performance was boosted from 87.2%±2.5% to 92.0%±1.6% using pre-trained ImageNet weights. In the second stage, another performance boost from 92.0%±1.6% to 93.4%±1.4% was observed as a result of fine-tuning the previous model on the UCSD dataset. Lastly, generating heatmaps provided additional proof for the effectiveness of our multi-scale structure, enabling the detection of retinal pathologies appearing in different sizes. CONCLUSION: The promising quantitative results of the proposed architecture, along with qualitative evaluations through generating heatmaps, prove the suitability of the proposed method to be used as a screening tool in healthcare centers assisting ophthalmologists in making better diagnostic decisions

ForensiBlock: A Provenance-Driven Blockchain Framework for Data Forensics and Auditability

Maintaining accurate provenance records is paramount in digital forensics, as they underpin evidence credibility and integrity, addressing essential aspects like accountability and reproducibility. Blockchains have several properties that can address these requirements. Previous systems utilized public blockchains, i.e., treated blockchain as a black box, and benefiting from the immutability property. However, the blockchain was accessible to everyone, giving rise to security concerns and moreover, efficient extraction of provenance faces challenges due to the enormous scale and complexity of digital data. This necessitates a tailored blockchain design for digital forensics. Our solution, Forensiblock has a novel design that automates investigation steps, ensures secure data access, traces data origins, preserves records, and expedites provenance extraction. Forensiblock incorporates Role-Based Access Control with Staged Authorization (RBAC-SA) and a distributed Merkle root for case tracking. These features support authorized resource access with an efficient retrieval of provenance records. Particularly, comparing two methods for extracting provenance records off chain storage retrieval with Merkle root verification and a brute-force search the offchain method is significantly better, especially as the blockchain size and number of cases increase. We also found that our distributed Merkle root creation slightly increases smart contract processing time but significantly improves history access. Overall, we show that Forensiblock offers secure, efficient, and reliable handling of digital forensic dataComment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl