The future challenges in modeling nano-photocatalysts involve combining different methods in a hierarchical way, a path that will provide understanding on how to design catalysts for producing fuel needed for a sustainable life in the future.

Antimicrobial peptides (AMPs) comprise a large family of peptides that include small cationic peptides, such as magainins, which permeabilize lipid membranes. Previous atomistic level simulations of magainin-H2 peptides show that they act by forming toroidal transmembrane pores. However, due to the atomistic level of description, these simulations were necessarily limited to small system sizes and sub-microsecond time scales. Here, we study the long-time relaxation properties of these pores by evolving the systems using a coarse-grain (CG) description. The disordered nature and the topology of the atomistic pores are maintained at the CG level. The peptides sample different orientations but at any given time, only a few peptides insert into the pore. Key states observed at the CG level are subsequently back-transformed to the atomistic level using a resolution-transformation protocol. The configurations sampled at the CG level are stable in the atomistic simulation. The effect of helicity on pore stability is investigated at the CG level and we find that partial helicity is required to form stable pores. We also show that the current CG scheme can be used to study spontaneous poration by magainin-H2 peptides. Overall, our simulations provide a multi-scale view of a fundamental biophysical membrane process involving a complex interplay between peptides and lipids.

In this review, we provide a short overview of the Molecular Dynamics (MD) method and how it can be used to model the water splitting process in photoelectrochemical hydrogen production. We cover classical non-reactive and reactive MD techniques as well as multiscale extensions combining classical MD with quantum chemical and continuum methods. Selected examples of MD investigations of various aqueous semiconductor interfaces with a special focus on TiO₂ are discussed. Finally, we identify gaps in the current state-of-the-art where further developments will be needed for better utilization of MD techniques in the field of water splitting.

As society moves towards a preventative approach to healthcare, there is growing interest in scientific research involving technology that can monitor and prevent adverse health outcomes. The primary objective of this paper is to develop an Internet of Things (IoT) wearable system based on Fried’s phenotype that is capable of detecting frailty. To determine user requirements, the system’s architecture was designed based on the findings of a questionnaire administered to individuals confirmed to be frail. A functional prototype was successfully developed and tested under real-world conditions. This paper introduces the methodology that was used to analyze the data collected from the prototype. It proposes an interdisciplinary approach to interpret wearable sensor data, providing a comprehensive overview through both visual representations and computational analyses facilitated by machine learning models. The findings of these analyses offer insights into the ways in which different types of activities can be classified and quantified as part of an overall physical activity level, which is recognized as an important indicator of frailty. The results provide the foundations for a new generation of affordable and non-intrusive systems able to detect and assess early signs of frailty.