The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research

The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocarriers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated

Wpływ warunków osadzania na właściwości filmów z CdTe

The aim of this work was to synthesize the CdTe thin film electrodes and to investigate the influence of the deposition conditions on the properties of CdTe films. Two different methods of CdTe formation were used: two-step electrochemical-chemical method and one-step electrochemical method. It was found, that the properties of CdTe films differ depending strictly on the experimental conditions during deposition such as: deposition potential, bath composition, temperature. Properties like chemical composition, crystallinity, band gap energy and morphology were investigated by various techniques such as Raman and Photoelectron Spectroscopy, X-ray Diffraction, UV-vis spectrometry, Scanning Electron Microscopy and Atomic Force Microscopy. The photoactivity of as-deposited CdTe films was investigated in the electrolyte solution which should protect them against photocorrosion. The results suggested that CdTe films prepared by both methods were unstable and undergo photooxidation. Therefore, further experiments were carried out with the application of polyindole (conducting polymer) layer as a protective coating against CdTe photocorrosion. The hybrid films polyindole-CdTe (PIN-CdTe) were prepared by the electropolymerization of indole on a Pt electrode followed by the electrodeposition of CdTe. The composition of the deposits was studied by Raman spectroscopy. SEM images revealed that in such deposits, the CdTe crystallites are formed either on the polymer films or they are embedded into the polymer layer. It was shown that polyindole layer improved the photocurrent and photostability of CdTe

The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research

The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocarriers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated

The Influence of Emerging Technologies on Distance Education

Recently, during the COVID-19 pandemic, distance education became mainstream. Many students were not prepared for this situation—they lacked equipment or were not even connected to the Internet. Schools and government institutions had to react quickly to allow students to learn remotely. They had to provide students with equipment (e.g., computers, tablets, and goggles) but also provide them with access to the Internet and other necessary tools. On the other hand, teachers were trying to adopt new technologies in the teaching process to enable more interactivity, mitigate feelings of isolation and disconnection, and enhance student engagement. New technologies, including Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Extended Reality (XR, so-called Metaverse), Big Data, Blockchain, and Free Space Optics (FSO) changed learning, teaching, and assessing. Despite that, some tools were implemented fast, and the COVID-19 pandemic was the trigger for this process; most of these technologies will be used further, even in classroom teaching in both schools and universities. This paper presents a concise review of the emerging technologies applied in distance education. The main emphasis was placed on their influence on the efficiency of the learning process and their psychological impact on users. It turned out that both students and teachers were satisfied with remote learning, while in the case of undergraduate children and high-school students, parents very often expressed their dissatisfaction. The limitation of the availability of remote learning is related to access to stable Internet and computer equipment, which turned out to be a rarity. In the current social context, the obtained results provided valuable insights into factors affecting the acceptance and emerging technologies applied in distance education. Finally, this paper suggests a research direction for the development of effective remote learning techniques

Application of the Chemical Leaching Method for the Recovery of Li and Co Contained in Spent Li-Ion Batteries

Waste batteries and accumulators are a group of waste, the amount of which is constantly increasing every year. A particular weight gain of this type of waste is observed for lithium-ion batteries used in various electronic devices and modern passenger vehicles. Due to the complex chemical composition and the content of different valuable metals, used Li-ion batteries should be subjected to appropriate recycling methods, the purpose of which is to separate the individual raw materials included in the battery. Regarding the demand for innovative technologies for processing spent Li-ion batteries, a concept of laboratory experiments was developed in the field of hydrometallurgical recovery of Li and Co contained in the battery powder obtained from this type of waste. As a result, it was shown that it is possible to effectively recover the tested metals with an adequately designed leaching process

The Application of Artificial Intelligence in the Effective Battery Life Cycle in the Closed Circular Economy Model—A Perspective

Global pollution of the environment is one of the most challenging environmental problems. Electronic-based population and anthropogenic activity are the main reasons for dramatically increasing the scale of waste generation, particularly battery waste. Improper battery waste disposal causes harmful environmental effects. Due to the release of heavy metals, battery waste affects ecosystems and health. We are faced with the challenge of effective battery waste management, especially recycling, to prevent the depletion of natural resources and maintain ecological balance. Artificial Intelligence (AI) is practically present in all areas of our lives. It enables the reduction of the costs associated with various types of research, increases automation, and accelerates productivity. This paper reviews the representative research progress of effective Artificial Intelligence-based battery waste management in the context of sustainable development, in particular, the analysis of current trends, algorithm accuracy, and data availability. Finally, the future lines of research and development directions of human-oriented Artificial Intelligence applications both in the battery production process and in battery waste management are discussed

The Application of Artificial Intelligence in the Effective Battery Life Cycle in the Closed Circular Economy Model—A Perspective

Global pollution of the environment is one of the most challenging environmental problems. Electronic-based population and anthropogenic activity are the main reasons for dramatically increasing the scale of waste generation, particularly battery waste. Improper battery waste disposal causes harmful environmental effects. Due to the release of heavy metals, battery waste affects ecosystems and health. We are faced with the challenge of effective battery waste management, especially recycling, to prevent the depletion of natural resources and maintain ecological balance. Artificial Intelligence (AI) is practically present in all areas of our lives. It enables the reduction of the costs associated with various types of research, increases automation, and accelerates productivity. This paper reviews the representative research progress of effective Artificial Intelligence-based battery waste management in the context of sustainable development, in particular, the analysis of current trends, algorithm accuracy, and data availability. Finally, the future lines of research and development directions of human-oriented Artificial Intelligence applications both in the battery production process and in battery waste management are discussed

Waste Management for Green Concrete Solutions: A Concise Critical Review

Reinforced concrete based on ordinary Portland cement (OPC) is one of the most commonly used materials in modern buildings. Due to the global growth of the building industry, concrete components have been partially or completely replaced with waste materials that can be used as binders or aggregates. Besides the ecological aspects, modern architecture widely needs materials to make the concrete durable, resisting large loads and various detrimental forces in the environment. This opens the possibilities of managing waste materials and applying them in practice. This paper presents a concise review of the green solutions for ecofriendly materials in the building industry that deal with the practical application of materials commonly treated as waste. The main emphasis was placed on their influence on the properties of the building material, optimal composition of mixtures, and discussion of the advantages and disadvantages of each of the “green” additives. It turned out that some solutions are far from being ecofriendly materials, as they leech and release numerous harmful chemicals into the environment during their presence in concrete. Finally, the paper suggests a research direction for the development of an ecofriendly structural material for a sustainable future

Photosensitive Thin Films Based on Drop Cast and Langmuir-Blodgett Hydrophilic and Hydrophobic CdS Nanoparticles

Comparative photoelectrochemical studies of cadmium sulfide (CdS) nanoparticles with either hydrophilic or hydrophobic surface properties are presented. Oleylamine organic shells provided CdS nanoparticles with hydrophobic behavior, affecting the photoelectrochemical properties of such nanostructured semiconductor. Hydrophilic CdS nanoparticles were drop-cast on the electrode, whereas the hydrophobic ones were transferred in a controlled manner with Langmuir-Blodgett technique. The substantial hindrance of photopotential and photocurrent was observed for L-B CdS films as compared to the hydrophilic, uncoated nanoparticles that were drop-cast directly on the electrode surface. The electron lifetime in both hydrophilic and hydrophobic nanocrystalline CdS was determined, revealing longer carrier lifetime for oleylamine coated CdS nanoparticles, ascribed to the trapping of charge at the interface of the organic shell/CdS nanoparticle and to the dominant influence of the resistance of the organic shell against the flux of charges. The “on” transients of the photocurrent responses, observed only for the oleylamine-coated nanoparticles, were resolved, yielding the potential-dependent rate constants of the redox processes occurring at the interface

Information and communication technologies combined with mixed reality as supporting tools in medical education

The dynamic COVID-19 pandemic has destabilized education and forced academic centers to explore non-traditional teaching modalities. A key challenge this creates is in reconciling the fact that hands-on time in lab settings has been shown to increase student understanding and peak their interests. Traditional visualization methods are already limited and topics such as 3D molecular structures remain difficult to understand. This is where advances in Information and Communication Technologies (ICT), including remote meetings, Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Extended Reality (XR, so-called Metaverse) offer vast potential to revolutionize the education landscape. Specifically, how MR merges real and virtual life in a uniquely promising way and offers opportunities for entirely new educational applications. In this paper, we briefly overview and report our initial experience using MR to teach medical and pharmacy students. We also explore the future usefulness of MR in pharmacy education. MR mimics real-world experiences both in distance education and traditional laboratory classes. We also propose ICT-based systems designed to run on the Microsoft HoloLens2 MR goggles and can be successfully applied in medical and pharmacy coursework. The models were developed and implemented in Autodesk Maya and exported to Unity. Our findings demonstrate that MR-based solutions can be an excellent alternative to traditional classes, notably in medicine, anatomy, organic chemistry, and biochemistry (especially 3D molecular structures), in both remote and traditional in-person teaching modalities. MR therefore has the potential to become an integral part of medical education in both remote learning and in-person study