A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors.

In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy.

Maximizing performance and efficiency in 3D printing of polylactic acid biomaterials: Unveiling of microstructural morphology, and implications of process parameters and modeling of the mechanical strength, surface roughness, print time, and print energy for fused filament fabricated (FFF) bioparts.

Medical stents, artificial teeth, and grafts are just some of the many applications for additive manufacturing techniques like bio-degradable polylactic acid 3D printing. However, there are drawbacks associated with fused filament fabrication-fabricated objects, including poor surface quality, insufficient mechanical strength, and a lengthy construction time for even a relatively small object. Thus, this study aims to identify the finest polylactic acid 3D printing parameters to maximize print quality while minimizing energy use, print time, flexural and tensile strengths, average surface roughness, and print time, respectively.

Investigation and Optimization of Effects of 3D Printer Process Parameters on Performance Parameters.

Professionals in industries are making progress in creating predictive techniques for evaluating critical characteristics and reactions of engineered materials. The objective of this investigation is to determine the optimal settings for a 3D printer made of acrylonitrile butadiene styrene (ABS) in terms of its conflicting responses (flexural strength (FS), tensile strength (TS), average surface roughness (Ra), print time (T), and energy consumption (E)). Layer thickness (LT), printing speed (PS), and infill density (ID) are all quantifiable characteristics that were chosen.

Erratum: Publisher Correction: Additive manufacturing for biomedical applications: a review on classification, energy consumption, and its appreciable role since COVID-19 pandemic.

[This corrects the article DOI: 10.1007/s40964-022-00373-9.].

Additive manufacturing for biomedical applications: a review on classification, energy consumption, and its appreciable role since COVID-19 pandemic.

The exponential rise of healthcare problems like human aging and road traffic accidents have developed an intrinsic challenge to biomedical sectors concerning the arrangement of patient-specific biomedical products. The additively manufactured implants and scaffolds have captured global attention over the last two decades concerning their printing quality and ease of manufacturing. However, the inherent challenges associated with additive manufacturing (AM) technologies, namely process selection, level of complexity, printing speed, resolution, biomaterial choice, and consumed energy, still pose several limitations on their use.

Applications of additive manufacturing (AM) in sustainable energy generation and battle against COVID-19 pandemic: The knowledge evolution of 3D printing.

Sustainable and cleaner manufacturing systems have found broad applications in industrial processes, especially aerospace, automotive and power generation. Conventional manufacturing methods are highly unsustainable regarding carbon emissions, energy consumption, material wastage, costly shipment and complex supply management. Besides, during global COVID-19 pandemic, advanced fabrication and management strategies were extremely required to fulfill the shortfall of basic and medical emergency supplies.

Additively manufactured nano-mechanical energy harvesting systems: advancements, potential applications, challenges and future perspectives.

Additively manufactured nano-MEH systems are widely used to harvest energy from renewable and sustainable energy sources such as wind, ocean, sunlight, raindrops, and ambient vibrations. A comprehensive study focusing on in-depth technology evolution, applications, problems, and future trends of specifically 3D printed nano-MEH systems with an energy point of view is rarely conducted. Therefore, this paper looks into the state-of-the-art technologies, energy harvesting sources/methods, performance, implementations, emerging applications, potential challenges, and future perspectives of additively manufactured nano-mechanical energy harvesting (3DP-NMEH) systems.

State-Of-The-Art and Trends in CO Laser Cutting of Polymeric Materials-A Review.

Carbon dioxide (CO) laser cutting finds one of its most relevant applications in the processing of a wide variety of polymeric materials like thermoplastics and thermosetting plastics. Different types of polymeric materials like polypropylene (PP), polymethyl methacrylate (PMMA), low- and high-density polyethylene (LDPE, HDPE), are processed by laser for different household as well as commercial products in the industry. The reason is their easy availability and economical aspect in the market.