Band 10, Heft 4

Pulmonary drug delivery (PDD) involves flow and deposition of aerosol particles acting as carriers of drugs delivered onto the surface of the airways. As a direct consequence, optimal PDD requires controlling of drug aerosolization processes and deep understanding of multiphase flows in complex geometry of the airways including aerosol particle dynamics during the transient inhalation cycles. A chemical engineering-based approache can be effectively used to analyze these processes and help in designing optimized drug formulations and more effective drug delivery devices (inhalers). One of prerequisites of improved PDD is the knowledge of in vivo–in vitro correlation (IVIVC) for inhaled drugs that would allow establishment of the relationships between aerosol quality determined using ex vivo methods (such as determination of particle size, deposition in reconstructed anatomical structures, pharmacokinetics/pharmacodynamics using in vitro cellular systems, or in silico modeling of aerosol dynamics) in connection to the clinical effects. This manuscript discusses the challenges of the IVIVC analyses for aerosol delivery systems. The primary focus is given to the physical and physicochemical constraints in the PDD that can be effectively described and investigated using engineering approaches.

Solar panels are essential for harvesting clean energy from the sun, but dust collection can dramatically limit their efficiency and power output. This challenge has led to the development of automated solar panel cleaning robots, a promising solution to ensure the continuous and optimal performance of solar arrays. One concern is that the cleaning method can cause long-term damage or degradation to the panels, such as cracking, if the cleaning robot is not positioned correctly on the solar panel. The risk of damage or degradation to solar panels during the cleaning process is a significant consideration for both manufacturers and operators of solar installations. Automated solar panel cleaning robots are valuable tools in maintaining the efficiency and longevity of solar arrays. However, their deployment requires a balanced approach that prioritizes effective cleaning while minimizing the risk of damage to the panels.

This study explores the development of graphene-reinforced poly (lactic acid) (PLA) composites as a sustainable material for electric vehicle (EV) battery casings. By incorporating graphene, a two-dimensional nanomaterial known for its exceptional mechanical and thermal properties, the PLA matrix exhibits significant improvements in performance. The optimized graphene content enhances the composite’s tensile strength, Young’s modulus, and ductility, ensuring robust mechanical protection for battery components. Additionally, graphene significantly increases the thermal conductivity of the composite, promoting effective heat dissipation and mitigating thermal degradation risks during battery operation. The research demonstrates the ability of graphene to act as a gas barrier, preventing the diffusion of oxygen and volatile by-products, which contributes to improved thermal stability and prolonged material durability. By tailoring the graphene loading and ensuring uniform dispersion, the composite achieves a balance between mechanical strength and thermal performance, making it ideal for lightweight, durable, and eco-friendly applications. This study underscores the potential of graphene-PLA composites in advancing sustainable technologies, particularly in the EV sector. It provides a foundation for future research aimed at optimizing fabrication methods, exploring synergistic filler combinations, and validating real-world performance to support the broader adoption of environmentally friendly materials in high-performance applications.

This chapter presents an innovative engineering approach to precision medicine in the treatment of hypothyroidism. By leveraging mathematical modeling of the hypothalamic-pituitary-thyroid (HPT) axis, a novel algorithm, PzeroT, is developed for individualized optimization of levothyroxine dosing. The chapter explores the HPT axis physiology and pathology and the current state-of-the-art in hypothyroidism treatment. The core contribution lies in implementing a compartmental model of the HPT axis, which is the foundation for the PzeroT algorithm. This model-based approach enables the prediction of the optimal levothyroxine dose tailored to individual patient characteristics. The chapter further validates the PzeroT algorithm using real patient case studies, demonstrating its potential to improve hypothyroidism management by reducing treatment times and enhancing dose accuracy compared to standard clinical practices. This work, at the intersection of chemical engineering and medicine, exemplifies how engineering principles can advance precision medicine and paves the way for more personalized and effective treatment of thyroid disorders.

This chapter discusses mechanical properties, electrical conductivity, and thermal stability of composite materials used in energy harvesting. It explores the use of composites in energy harvesters that are thermoelectric, electromagnetic, and piezoelectric. One of the main advantages of composite materials for energy harvesting is their high strength-to-weight ratio. Metal- and polymer-based composites are among the types of composites. The piece also explores the challenges that need to be solved for the technology to realize its full potential. If composite materials were less expensive and more durable, energy collecting systems might be more dependable and efficient. To ascertain the resilience and reliability of composite materials for application in energy harvesting, more research is necessary. Composite materials, however, have potential for energy harvesting. However, composite materials show promise for energy harvesting. Before they can reach their full potential, more research and development are needed. The major obstacles and opportunities for further research and development in composite materials for energy harvesting are covered in this chapter.

Haemophilia B, also known as the Christmas disease, named after Stephen Christmas the first patient diagnosed with this disease, is an inherited disease caused by a defect in the Factor IX Gene ( F9 ). This defect manifests in insufficient production of the blood coagulation factor IX, resulting in excessive bleeding. The therapy which is mainly used involves prophylactic infusions of factor IX concentrate to improve the quality of life by minimising the episodes of bleeds. The main limitations of such a treatment plan are repeat infusions, product half-life, costs and inhibitor formation. The FIX concentration in healthy individuals is typically 90 nM and based upon the experience of the clinicians increasing FIX activity to 1–5 % of normal values has significant impact on patients’ quality of life. Therefore, even a partial correction the FIX deficiency would result in improved clinical outcomes and increase the chances of patients living a near-normal life. Gene therapy has the potential to deliver this, and the fact that haemophilia B is monogenic in nature further encourages the exploration of gene delivery for this disease. In this chapter, an integrated Pharmacokinetic (PK) – Pharmacodynamic (PD) model that has been developed using the clinical data is reported. The key features of the model are that it considers the pharmacological response, i.e., plasma FIX coagulation activity level as well as the toxicological response, i.e., the level of serum alanine aminotransferase. The simulation-based PK-PD modelling approach is then used for the initial dose selection to provide clinicians with better tools to simplify the decision-making process for designing more effective treatment plans, which can be tailored to maximise efficacy while minimising toxicity for individual patients.