Extending the uses of lipid-membrane coated electrodes: Next generation of lipid membrane biosensors and smart implantable cell-electrode devices

University of Technology Sydney. Faculty of Science.The ability to combine both a functional sensing and signalling membrane-electrode interface system is crucial for developing new technologies that can directly connect the living biosphere with electrical devices. However, there is a considerable distinction between both the chemical and biomechanical properties of live cell membranes versus synthetic electrical prostheses, thus there remain significant challenges that must be overcome in order to establish stable and functionally predictable interactions between these different components. The sparsely tethered bilayer lipid membrane possesses the necessary skeleton onto which novel chemistries can be added in order to succeed in the first iteration of correctly integrating electronic coupling with biological tissue. This dissertation presents an investigation into controlling the ionic and the electronic interface and then detecting ion fluxes arising from nearby biologically active cells at the nanometer scale, by using the detectable electrical signals derived from interfacing of membranes with a gold electrode. In it, the feasibility of implementing tBLMs as either an interface between biological systems and electrical devices or for continual sensing in real-time or for diagnostic purposes is investigated. Commencing is a comprehensive review of variant artificial lipid membrane models and the impedance spectroscopy approach (Chapter 1). A demonstration of the intimate nanoscale contacts of cells with the surface of the electrode is presented in Chapter 2. The aim of this study was to examine the feasibility of applying tBLMs in bio-implantable devices to offer specific transmission of electrical signals to individual target neurons to improve signal fidelity. This was to be achieved by reducing leakage pathways, thereby minimizing electrophoretic ion currents being lost into the surrounding interstitial medium. Chapter 3 describes how, instead of using the lipid membrane-covered electrodes to signal to cells, the electrode might be used to as a nano-biosensor for cell detection. Various approaches to increase sensitivity were explored to enhance this capability. The necessity for detection at the nanometer scale is explored in Chapter 4, recording in real-time the laser-generated heat pulses arising from laser-illuminated gold nanoparticles. Detection of these heat pulses required attachment of the gold nanoparticles to the membrane surface, while non-specific binding of gold nanoparticles failed to elicit a measurable response. Conclusions and perspectives are presented in Chapter 5, sum up of the significant achievements presented in this dissertation, which has focused on extending our understanding of cell membrane interactions and exploring the feasibility of using these across a range of applications

The use of tethered bilayer lipid membranes to identify the mechanisms of antimicrobial peptide interactions with lipid bilayers

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This review identifies the ways in which tethered bilayer lipid membranes (tBLMs) can be used for the identification of the actions of antimicrobials against lipid bilayers. Much of the new research in this area has originated, or included researchers from, the southern hemisphere, Australia and New Zealand in particular. More and more, tBLMs are replacing liposome release assays, black lipid membranes and patch-clamp electrophysiological techniques because they use fewer reagents, are able to obtain results far more quickly and can provide a uniformity of responses with fewer artefacts. In this work, we describe how tBLM technology can and has been used to identify the actions of numerous antimicrobial agents

Triple Junction Segregation Dominates the Stability of Nanocrystalline Alloys.

We present large-scale atomistic simulations that reveal triple junction (TJ) segregation in Pt-Au nanocrystalline alloys in agreement with experimental observations. While existing studies suggest grain boundary solute segregation as a route to thermally stabilize nanocrystalline materials with respect to grain coarsening, here we quantitatively show that it is specifically the segregation to TJs that dominates the observed stability of these alloys. Our results reveal that doping the TJs renders them immobile, thereby locking the grain boundary network and hindering its evolution. In dilute alloys, it is shown that grain boundary and TJ segregation are not as effective in mitigating grain coarsening, as the solute content is not sufficient to dope and pin all grain boundaries and TJs. Our work highlights the need to account for TJ segregation effects in order to understand and predict the evolution of nanocrystalline alloys under extreme environments

Serum Vitamin D and Vaspin Levels Among Patients with Acute Myocardial Infarction and Their Association with Risk Factors

Mukhtiar Baig,1 Kamal Waheeb Alghalayini,2 Zohair Jamil Gazzaz,3 Manal Abdulaziz Murad4 1Department of Clinical Biochemistry, Faculty of Medicine, Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia; 2Department of Internal Medicine, Consultant Cardiologist, King Abdulaziz University Hospital, Jeddah, Saudi Arabia; 3Department of Internal Medicine, Faculty of Medicine, Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia; 4Department of Family and Community Medicine, Faculty of Medicine, Rabigh, King Abdulaziz University, Jeddah, Saudi ArabiaCorrespondence: Mukhtiar Baig, Department of Clinical Biochemistry, Faculty of Medicine in Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia, Tel +966581083289, Email [email protected]: The current study investigated and compared serum levels of vitamin D (VD) and vaspin in AMI patients and healthy subjects and correlated these biomarkers with other biochemical risk factors for AMI.Patients and Methods: The research was carried out at King Abdulaziz University Hospital (KAUH) in Jeddah. Blood samples and additional information were gathered from 110 admitted AMI patients in the Intensive Coronary Care Unit (ICCU) (ages 40– 65 years) and 50 adult, healthy volunteers whose BMI and age were similar to those of the patients.Results: AMI patients had significantly lower vaspin (p 20 - < 30 ng/mL), and 35 (31.8%) had sufficient levels (≥ 30 ng/mL). In healthy subjects, VD levels were deficient in 4(8%), insufficient in 13 (26%), and sufficient in 33 (66%). VD insufficiency was more prevalent in AMI patients compared to the healthy group (54.5% vs 26%; p < 0.001). In AMI patients, serum vaspin was found to be related to age and HbA1c in the control group. VD did not show a significant correlation with any variable in AMI patients and healthy subjects. Serum vaspin (p = 0.89) and VD levels (p = 0.29) did not differ significantly between female and male control groups.Conclusion: Compared to the healthy group, AMI patients showed significantly lower vaspin and VD levels. Additionally, AMI patients had a higher prevalence of VD deficiency and insufficiency, suggesting its possible role in the occurrence of AMI.Keywords: vitamin D, vaspin, myocardial infarction, BMI, vitamin D deficiency, HDL-

Cholic Acid-Based Antimicrobial Peptide Mimics as Antibacterial Agents

There is a significant and urgent need for the development of novel antibacterial agents to tackle the increasing incidence of antibiotic resistance. Cholic acid-based small molecular antimicrobial peptide mimics are reported as potential new leads to treat bacterial infection. Here, we describe the design, synthesis and biological evaluation of cholic acid-based small molecular antimicrobial peptide mimics. The synthesis of cholic acid analogues involves the attachment of a hydrophobic moiety at the carboxyl terminal of the cholic acid scaffold, followed by the installation of one to three amino acid residues on the hydroxyl groups present on the cholic acid scaffold. Structure–activity relationship studies suggest that the tryptophan moiety is important for high antibacterial activity. Moreover, a minimum of +2 charge is also important for antimicrobial activity. In particular, analogues containing lysine-like residues showed the highest antibacterial potency against Gram-positive S. aureus. All di-substituted analogues possess high antimicrobial activity against both Gram-positive S. aureus as well as Gram-negative E. coli and P. aeruginosa. Analogues 17c and 17d with a combination of these features were found to be the most potent in this study. These compounds were able to depolarise the bacterial membrane, suggesting that they are potential antimicrobial pore forming agents

Frontiers in Energy Storage: Next-Generation Artificial Intelligence (AI)

The Department of Energy's (DOE) Office of Electricity (OE) sponsored the "Frontiers in Energy Storage: Next-Generation Artificial Intelligence (AI) Workshop", which was hosted at Lawrence Berkeley National Laboratory on April 16, 2024. This hybrid event convened industry leaders, researchers, and innovators both in-person and virtually to discuss the transformative potential of AI in enhancing the development and adoption of grid-scale energy storage. Participants presented and discussed how advancements in machine learning (ML) and AI can catalyze innovation in material development, system integration and optimization, performance validation, and strategic policy development. The wide-ranging workshop spanned topics from accelerated materials development to policy and valuation of long duration energy storage systems as well as the use of AI-powered agentic systems to manage grid operations. Throughout the event, participants highlighted the technical, social, and financial hurdles in building and maintaining robust data infrastructures that serve as the foundation for AI/ML innovation. Key recommendations were made across the entire range of topics, with clear needs for the development of scalable and trustable AI tools, enhancement of data availability and interoperability, the need for interpretability and transparency in policy decisions, and promotion of cross-sector collaboration to realize the full potential of AI in energy storage. Additionally, the workshop underscored the importance of fostering collaboration between academic/government researchers and industry to fully unlock the potential of AI in energy storage and grid operations. This report summarizes these discussions, with the goal to guide and inform future advancements of AI for energy storage that align with national goals for energy efficiency and sustainability

A Value-Based Sequential Optimization Framework for Efficient Materials Design Considering Uncertainty and Variability

Many problems in engineering and science can be framed as decision problems in which we choose values for decision variables that lead to desired outcomes. Notable examples include maximizing lift in airplane wing design, improving the efficiency of a power plant, or identifying processing protocols resulting in structural materials with desired mechanical properties. These problems typically involve a significant degree of uncertainty about the often-complex underlying relationships between the decision variables and the outcomes. Solving such decision problems involves the use of computational models or physical experimentation to generate data to make predictions and test hypotheses. As a result, both approaches incur costs in terms of data generation and collection that must be considered when assessing the trade-off between the benefits of these data and the cost of generating it. This is especially true when there is variability in the generated results. This variability requires multiple measurements to characterize the outcome statistically, further increasing the costs. This can often result in suboptimal solutions because the optimization process is prematurely stopped due to the high costs incurred. To overcome these challenges, we develop a value-based data-driven optimization framework to solve such complex decision problems efficiently. Typically, the computational or physical experiments needed to solve these problems are spread over the entire design space to explore it or focused on subregions to exploit them and find an optimum. Since the experimentation costs are high, these costs need to be considered during optimization. One way to accomplish this is by taking advantage of the fact that in many problems, simultaneously performing multiple experiments incurs less total cost than when performed individually. Another way to accomplish it is to choose to collect data, whether computational or physical, that maximize the expected value gained from them. This balances the exploration and exploitation and allows the framework to identify the solution efficiently. The developed framework starts with design space exploration and gradually transitions to an exploitation stage as the framework gains a better understanding of the problem. This is achieved by incrementally performing experiments and using their results to help choose the next set of experiments while considering the associated expenses. This iterative approach leads to a multistage stochastic programming method, allowing for a guided and targeted experimentation approach that improves the overall efficiency of the optimization framework. We develop the framework in the context of materials design efforts, specifically the Laser-Powder Bed Fusion (L-PBF) additive manufacturing process. We adopt the view that materials design entails deciding on the processing conditions that optimize the engineering properties of the manufactured materials systems. Materials design is chosen as the application of interest because traditional design methods are inefficient and time-consuming, as they rely on trial-and-error experimentation. More specifically, the motivating design problem considered in this work is to identify the laser process parameters for L-PBF to achieve a combination of mechanical properties that maximize the value to the user, customer, or manufacturer. We test the framework using a series of synthetic profiles to quantify and compare its performance with currently used optimization algorithms for L-PBF. The synthetic profiles vary differently within the design space and are considered to represent the trends of the mechanical properties in the space. These synthetic profiles are combined to yield outcome surfaces of the material value with varying degrees of complexity. Simulation results show that the framework follows a balanced exploration-exploitation optimization scheme. Additionally, comparison with currently used optimization methods for L-PBF shows that the framework outperforms the latter method. The framework requires fewer experiments to identify a material with a better material value. Applying the framework to complex problems in materials design and other fields leads to many benefits, opening new research avenues and possibilities for optimization in practice. The framework can yield materials with enhanced properties when applied to the L-PBF process and many other materials synthesis and processing techniques with several design variables. The framework improves the search efficiency for an optimal solution by identifying an optimal outcome using less time and experimentation resources. This makes the framework a valuable tool for addressing a wide range of real-world challenges, where complex relationships exist between decision variables and outcomes and where solving these challenges requires resource-intensive approaches

Improving an internal material handling system. A case study of a Swedish company in food industry

Currently, customers are exerting a lot of pressure on companies by demanding for best product quality, customized products, reduced product lead time and reliable product delivery. Therefore, for companies to be highly competitive, there is need to improve productivity and delivery performance by having an efficient material flow. Nevertheless, the task of making the material to flow efficiently throughout the manufacturing process up to when the customer receives the product is not easy. To solve this, companies are focusing on the material handling system as it has an impact on efficient material flow and productivity. Hence, the aim of this project was to explore how an internal material handling system can be improved to guarantee a better delivery performance. To fulfil the aim, a single case study was undertaken at a dairy food producing company in Jonkoping. The information on the subject area was obtained through interviews, observation at the company and an extensive literature review. The information that was obtained was assessed in accordance with the framework of the project that includes; principles and physical elements used for designing a material handling system, software and information, and human and management. Combined analysis of the findings from the empirical study and the extensive literature review helped to identify the problems faced in an internal material handling system of the company. This was followed by identifying ways of improving material handling system and thereafter, improvement suggestions were made targeting enhancement of the delivery performance of the system. In conclusion, the findings indicate that improvement of an internal material handling system does not only depend on improving the physical attributes of the system, far from it, it is more dependent on having an efficient and effective information system. Another factors that came out is that there should be a proper integration of the material handling system and the workers operating the system. From a systems perspective this research has added information sharing and human and management to the one dimensional physical elements improvement of a material handling system

Understanding the Role of Grain Boundaries in Nanoscale Sintering

Sintering is a processing technique used to consolidate a powder compact and convert it into one with strength and structural integrity. Temperature, pressure, interface structure, and size of initial particles are all factors that contribute to sintering kinetics. Recently, sintering of nanoscale particles has been an area of active research, as it presents many potential advantages, such as lower temperatures, shorter processing times, and the ability to fabricate materials with tailored microstructures, and thus optimal properties. In crystalline materials, when differently oriented particles bond, internal interfaces (i.e., grain boundaries) form. As the particle size is reduced into the nanoscale, the role of grain boundaries (GBs) becomes key due to the increased surface-to-volume ratio and the number of contact points between particles. Herein, atomistic simulations are leveraged to fundamentally understand the role of the grain boundary structure and properties in sintering kinetics. Simulation results reveal a plethora of densification profiles ranging from rapid densification to stagnant behavior depending on the GBs present in the systems. On the whole, our modeling approach provides future avenues to explore the role of interfaces in the sintering behavior of nanoscale powders