Plasma electrolytic oxidation (PEO) as surface treatment for high strength Al alloys produced by L-PBF: Microstructure, performance, and effect of substrate surface roughness

In recent years, additive manufacturing of Aluminium alloys has achieved remarkable developments, allowing for the replacement of casted components in industrial fields such as aerospace and automotive. However, the main issue affecting these alloys during operation at high temperatures and in critical environments is poor corrosion and wear resistance. The present work aims to produce a coated layer using an innovative surface treatment, Plasma Electrolytic Oxidation (PEO), on two high-strength Al alloys, AlSi10Mg and A205, processed by Laser-Powder Bed Fusion (L-PBF), in order to increase the corrosion and wear performance of the material. For each material, PEO coating was produced on two different surface conditions (as-fabricated and polished) and characterised in terms of morphology and composition through scanning electron microscopy (SEM) and digital microscope analysis. A PEO coating thickness of over 40 μm was achieved for both alloys, while the porosity was found around 13 % and 3 % for AlSi10Mg and A205, respectively. Additionally, nano-hardness analyses were carried out to understand the differences compared to the virgin material, highlighting an increase in hardness of the PEO coating at least 10 greater than the substrate for both materials. Finally, friction and corrosion tests were performed. The results in terms of wear rate and corrosion rate were compared with those obtained on uncoated manufactured samples. In particular, an increase in the wear and corrosion performance of 26.4 % and 37.5 %, respectively for the AlSi10Mg, and 88.4 % and 53.1 % for the A205, were evaluated. It was demonstrated that the presence of the oxidised layer improved the mechanical properties of the surface and, accordingly, the general performance of the material. Furthermore, performing a surface polishing treatment before PEO treatment helped to further increase the tribological and corrosion properties

A novel porosity prediction framework based on reinforcement learning for process parameter optimization in additive manufacturing

Machine learning (ML) has generated great interest in additive manufacturing (AM) thanks to its ability to predict complex patterns and behaviors through data. Examples include design optimization, process control, and cost minimization. In this paper, we develop a novel framework based on reinforcement learning (RL) for porosity prediction in metal laser-powder bed fusion (L-PBF). The novelty of this approach is twofold: it is the first approach that integrates RL in L-PBF for porosity prediction where the state space consists of permutations of three parameters (laser power, scan speed, and hatch spacing) for optimal parameter combinations; furthermore, through an appropriately formulated reward function, we embed physics-informed principles based on the Eagar-Tsai thermal model for training. The proposed framework has been experimentally validated on L-PBF high-strength A205 Al alloy. The experimental results demonstrated high fidelity with the predicted optimal parameters, despite few outliers, demonstrating the potential of this approach

FFF-based high-throughput sequence shortlisting to support the development of aptamer-based analytical strategies

Aptamers are biomimetic receptors that are increasingly exploited for the development of optical and electrochemical aptasensors. They are selected in vitro by the SELEX (Systematic Evolution of Ligands by Exponential Enrichment) procedure, but although they are promising recognition elements, for their reliable applicability for analytical purposes, one cannot ignore sample components that cause matrix effects. This particularly applies when different SELEX-selected aptamers and related truncated sequences are available for a certain target, and the choice of the aptamer should be driven by the specific downstream application. In this context, the present work aimed at investigating the potentialities of asymmetrical flow field-flow fractionation (AF4) with UV detection for the development of a screening method of a large number of anti-lysozyme aptamers towards lysozyme, including randomized sequences and an interfering agent (serum albumin). The possibility to work in native conditions and selectively monitor the evolution of untagged aptamer signal as a result of aptamer-protein binding makes the devised method effective as a strategy for shortlisting the most promising aptamers both in terms of affinity and in terms of selectivity, to support subsequent development of aptamer-based analytical devices. Graphical abstract: [Figure not available: see fulltext.

High Temperature Electron Localization in dense He Gas

We report new accurate mesasurements of the mobility of excess electrons in high density Helium gas in extended ranges of temperature $[(26\leq T\leq 77) K ]$ and density $[ (0.05\leq N\leq 12.0) {atoms} \cdot {nm}^{-3}]$ to ascertain the effect of temperature on the formation and dynamics of localized electron states. The main result of the experiment is that the formation of localized states essentially depends on the relative balance of fluid dilation energy, repulsive electron-atom interaction energy, and thermal energy. As a consequence, the onset of localization depends on the medium disorder through gas temperature and density. It appears that the transition from delocalized to localized states shifts to larger densities as the temperature is increased. This behavior can be understood in terms of a simple model of electron self-trapping in a spherically symmetric square well.Comment: 23 pages, 13 figure

Machining and heat treatment as post-processing strategies for Ni-superalloys structures fabricated using direct energy deposition

The aim of this study is to determine the most suitable post-processing routines to enhance the surface integrity of components produced with Inconel 718 superalloy by additive manufacturing. The components were fabri­cated by Direct Energy Deposition (DED) followed by two typical post-processing methods: machining and heat treatment. The effect of the post-processing sequence (machining + heat treatment or heat treat­ment + machining) and the corresponding effects on the surface integrity of these components were investigated in terms of surface finishing, microstructure, micro-hardness and residual stresses. Finally, suitable solutions in terms of additive manufacturing - post-process operations have been reported

Identification and quantification of phenolic compounds in bambangan (Mangifera pajang Kort.) peels and their free radical scavenging activity.

Phenolic compounds and antioxidant capacity of acidified methanolic extract prepared from fully ripe bambangan (Mangifera pajang K.) peel cultivated in Sarawak, Malaysia, were analyzed. The total phenolic content (98.3 mg GAE/g) of bambangan peel powder (BPP) was determined by the Folin-Ciocalteu method. BPP showed a strong potency of antioxidant activity and was consistent with that of BHT and vitamin C as confirmed by the DPPH (1,1-diphenyl-2-picrylhydrazyl) radical scavenging activity and FRAP (ferric-reducing antioxidant power) assays. Gallic acid, p-coumaric acid, ellagic acid, protocatechuic acid, and mangiferin were the major compounds among the 16 phenolics that have been identified and quantified in M. pajang peels with 20.9, 12.7, 7.3, 5.4, and 4.8 mg/g BPP, respectively. Peak identities were confirmed by comparing their retention times, UV-vis absorption spectra, and mass spectra with authentic standards. The 16 phenolic compounds identified in M. pajang K. using HPLC-DAD and TSQ-ESI-MS are reported here for the first time

Superhydrophobic lab-on-chip measures secretome protonation state and provides a personalized risk assessment of sporadic tumour

Secretome of primary cultures is an accessible source of biological markers compared to more complex and less decipherable mixtures such as serum or plasma. The protonation state (PS) of secretome reflects the metabolism of cells and can be used for cancer early detection. Here, we demonstrate a superhydrophobic organic electrochemical device that measures PS in a drop of secretome derived from liquid biopsies. Using data from the sensor and principal component analysis (PCA), we developed algorithms able to efficiently discriminate tumour patients from non-tumour patients. We then validated the results using mass spectrometry and biochemical analysis of samples. For the 36 patients across three independent cohorts, the method identified tumour patients with high sensitivity and identification as high as 100% (no false positives) with declared subjects at-risk, for sporadic cancer onset, by intermediate values of PS. This assay could impact on cancer risk management, individual’s diagnosis and/or help clarify risk in healthy populations