Chapter 4 Health security as a public good in the era of the Fourth Industrial Revolution in Poland

The fourth industrial revolution, characterized by digitization, artificial intelligence and augmented reality, and megatrends such as globalization, urbanization, demographic changes and the knowledge-based economy, will trigger a series of profound technological, economic, social and environmental changes that will permanently and irreversibly change the role of the state in meeting social needs. Industry 4.0 will also change the type, nature, and scope of public goods and how they are produced, financed, delivered, and consumed. This book redefines the current paradigm of public goods. It proposes a model of production and distribution of public goods that acknowledges the participation of entities from the public, private, and nonprofit sectors. The authors argue that these entities would participate in the production, financing, distribution, and consumption of such goods. From a theoretical point of view, such an inclusive approach involving the expansion of the classical state - market dichotomy with new entities, including citizens themselves, leads to a new conceptualization and approach towards public goods. The model assumes shared responsibility, subsidiarity, and paternalistic libertarianism, and it allows the state to create new entities of an educational or fiscal nature, while remaining the regulator of public services and distribution. Additionally, the book analyzes changes regarding the perception of public goods, in the era of the fourth industrial revolution, across selected sectors such as healthcare and pension systems, education, local public goods, and public utility services. The book is primarily addressed to researchers, scholars and students across social and technical sciences, and it will also be a useful guide for central and local administration bodies responsible for public policy

Decoding reactive structures in dilute alloy catalysts

Rational catalyst design is crucial toward achieving more energy-efficient and sustainable catalytic processes. Understanding and modeling catalytic reaction pathways and kinetics require atomic level knowledge of the active sites. These structures often change dynamically during reactions and are difficult to decipher. A prototypical example is the hydrogen-deuterium exchange reaction catalyzed by dilute Pd-in-Au alloy nanoparticles. From a combination of catalytic activity measurements, machine learning-enabled spectroscopic analysis, and first-principles based kinetic modeling, we demonstrate that the active species are surface Pd ensembles containing only a few (from 1 to 3) Pd atoms. These species simultaneously explain the observed X-ray spectra and equate the experimental and theoretical values of the apparent activation energy. Remarkably, we find that the catalytic activity can be tuned on demand by controlling the size of the Pd ensembles through catalyst pretreatment. Our data-driven multimodal approach enables decoding of reactive structures in complex and dynamic alloy catalysts