Advances in biodegradable materials: Degradation mechanisms, mechanical properties, and biocompatibility for orthopedic applications.

Mg-based and Zn-based biodegradable materials have the potential to become the next-generation implant materials to treat bone diseases, because of their desired degradation and mechanical properties. This article reviews the status of these implant materials. The required properties of biodegradable materials such as biodegradability, mechanical properties, and biocompatibility for performance evaluation were briefly discussed.

Managing energy transition alongside environmental protection by making use of AI-led butanol powered SI engine optimization in compliance with SDGs.

Enormous consumption of fossil fuel resources has risked energy accessibility in the upcoming years. The price fluctuation and depletion rate of fossil fuels instigate the urgent need for searching their reliable substitute. The current study tries to address these issues by presenting butanol as a replacement for gasoline in SI engines at various speeds and loading conditions.

Innovative technologies for removal of micro plastic: A review of recent advances.

Plastics are becoming a pervasive pollutant in every environmental matrix, particularly in the aquatic environment. Due to increased plastic usage and its impact on human and aquatic life, microplastic (MP) pollution has been studied extensively as a global issue. The production of MP has been linked to both consumer and commercial practices.

Mo-based MXenes: Synthesis, properties, and applications.

Ti-MXene allows a range of possibilities to tune their compositional stoichiometry due to their electronic and electrochemical properties. Other than conventionally explored Ti-MXene, there have been ample opportunities for the non-Ti-based MXenes, especially the emerging Mo-based MXenes. Mo-MXenes are established to be remarkable with optoelectronic and electrochemical properties, tuned energy, catalysis, and sensing applications.

Experiments and CFD simulation of an air-conditioned tractor cabin for thermal comfort of tractor operators in Pakistan.

Tractors are manufactured without air-conditioned cabins in Pakistan. This leads to thermal discomfort for tractor operators working under direct solar exposure. Therefore, this study aimed to design and install an air-conditioned cabin on a tractor.

A Hybrid Data-Driven Metaheuristic Framework to Optimize Strain of Lattice Structures Proceeded by Additive Manufacturing.

This research is centered on optimizing the mechanical properties of additively manufactured (AM) lattice structures via strain optimization by controlling different design and process parameters such as stress, unit cell size, total height, width, and relative density. In this regard, numerous topologies, including sea urchin (open cell) structure, honeycomb, and Kelvin structures simple, round, and crossbar (2 × 2), were considered that were fabricated using different materials such as plastics (PLA, PA12), metal (316L stainless steel), and polymer (thiol-ene) via numerous AM technologies, including stereolithography (SLA), multijet fusion (MJF), fused deposition modeling (FDM), direct metal laser sintering (DMLS), and selective laser melting (SLM). The developed deep-learning-driven genetic metaheuristic algorithm was able to achieve a particular strain value for a considered topology of the lattice structure by controlling the considered input parameters.

Evaluating the Stress-Strain Relationship of the Additively Manufactured Lattice Structures.

Extensive amount of research on additively manufactured (AM) lattice structures has been made to develop a generalized model that can interpret how strongly operational variables affect mechanical properties. However, the currently used techniques such as physics models and multi-physics simulations provide a specific interpretation of those qualities, and are not general enough to assess the mechanical properties of AM lattice structures of different topologies produced on different materials via several fabrication methods. To tackle this problem, this study develops an optimal deep learning (DL) model based on more than 4000 data points, which has been optimized by analyzing three different hyper-parameters optimization schemes including gradient boost regression trees (GBRT), gaussian process (GP), and random forest (RF) with different data distribution schemes such as normal distribution, nth root transformation, and robust scaler.

On the Critical Heat Flux Assessment of Micro- and Nanoscale Roughened Surfaces.

The boiling crisis or critical heat flux (CHF) is a very critical constraint for any heat-flux-controlled boiling system. The existing methods (physical models and empirical correlations) offer a specific interpretation of the boiling phenomenon, as many of these correlations are considerably influenced by operational variables and surface morphologies. A generalized correlation is virtually unavailable.

Boiling Heat Transfer Evaluation in Nanoporous Surface Coatings.

The present study develops a deep learning method for predicting the boiling heat transfer coefficient (HTC) of nanoporous coated surfaces. Nanoporous coated surfaces have been used extensively over the years to improve the performance of the boiling process. Despite the large amount of experimental data on pool boiling of coated nanoporous surfaces, precise mathematical-empirical approaches have not been developed to estimate the HTC.