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Aiming to achieve efficient activity against severe acute respiratory syndrome coronavirus (SARS-CoV-2), the expansion of the structure- and ligand-based drug design approaches was adopted, which has been recently reported by our research group. Purine ring is a corner stone in the development of SARS-CoV-2 main protease (Mpro) inhibitors. The privileged purine scaffold was elaborated to achieve additional affinity based on hybridization and fragment-based approaches. Thus, the characteristic pharmacophoric features that are required for the inhibition of Mproand RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 were utilized along with the crystal structure information of both targets. The designed pathways involved rationalized hybridization with large sulfonamide moieties and a carboxamide fragment for the synthesis of ten new dimethylxanthine derivatives. The synthesis was performed under diverse conditions to afford N-alkylated xanthine derivatives, and cyclization afforded tricyclic compounds. Molecular modeling simulations were used to confirm and gain insights into the binding interactions at both targets' active sites. The merit of designed compounds and the in silicostudies resulted in the selection of three compounds that were evaluated in vitroto estimate their antiviral activity against SARS-CoV-2 (compounds 5, 9aand 19with IC50values of 38.39, 8.86 and 16.01 μM, respectively). Furthermore, oral toxicity of the selected antiviral candidates was predicted, in addition to cytotoxicity investigations. Compound 9ashowed IC50values of 8.06 and 3.22 μM against Mproand RdRp of SARS-CoV-2, respectively, in addition to promising molecular dynamics stability in both target active sites. The current findings encourage further specificity evaluations of the promising compounds for confirming their specific protein targeting.

In-situ monitoring and assessment of thermal evolution during the layer-by-layer build process of a laser powder bed fusion (L-PBF) additive manufacturing system play a pivotal role to help understand the process-structure-property correlation of the L-PBF. Interlayer temperature refers to the layer temperature after the powder is spread but before scanning a new layer commences. It represents the part heating due to the processing of the previous layers and acts as the initial temperature under which a new layer is scanned. Therefore, interlayer temperature is essential for the derivation of process control to minimize keyholes and other thermal-related defects. In addition, measurements of interlayer temperature can be used for validating part-scale thermal modeling or for detecting part defects. This paper presents an experimental study of the evolution of interlayer temperature through in-situ thermographic imaging during the fabrication of twin square-canonical parts of Inconel 718 using the EOS M280 system. Post-process distortion measurements of the fabricated parts are also obtained to provide insights into the correlation to geometric features of the part. The experimental results show that the evolution of the interlayer temperature highly correlates with the unique geometric features of the part and the support structure used to build the part. During the processing of the square-canonicals, the interlayer temperature reached as high as 325 °C, which is significantly higher than the preheated substrate temperature of 80 °C under which the first layer of the part is scanned. Measurements of the build after manufacturing show that the largest normal displacement of the part's outer wall surface reached about 17 % of its thickness. The results also show that the peak distortion and peak interlayer temperature do not occur at the same layer and are due to different causes.

In-situ monitoring techniques for additive manufacturing are in high demand to help produce reliable parts. The structural integrity of these parts depends on both the presence of flaws and their microstructure. Ultrasonic Rayleigh waves have the potential to identify flaws and assess the local microstructure during directed energy deposition (DED) additive manufacturing processes, but the scattering associated with the surface roughness degrades the ultrasonic signal and must be understood to extract useful information. Herein, the microstructures and surface profiles of DED and wrought Ti-6Al-4V are compared to provide context for measured Rayleigh wave speeds and second harmonic generation. The Rayleigh wave speed and second harmonic generation for DED and wrought Ti-6Al-4V materials having comparable surface roughness are significantly different. The wave speed measured in DED material is 3% slower than in wrought material, and the relative nonlinearity parameter, commonly used to characterize second harmonic generation, is 3.5-6.0 times higher for polished surfaces. Wave speed and second harmonic generation measurements are also made along the hatch and across the hatch for both as-built and glazed DED surfaces. Based on our results, we conclude that in-situ Rayleigh wave linear and nonlinear measurements are possible; although we acknowledge that in-situ angle-beam transducer generation would be challenging, and thus we will investigate pulsed laser generation in future work.

The goal of this work was to investigate the effect of gamma irradiated entomopathogenic nematode EPN; Steinernema scapteriscion third and fourth larval instars of Culex pipiensunder laboratory conditions and its effect on the elemental composition of the treated larvae. Results showed that normal and irradiated tested nematodes differed in their efficacies against third and fourth larval instars. The mortality rates increased with increasing the nematode concentration and the period after treatment, in most cases the mortality in fourth instar larvae was more than in third instar larvae under the same treatments, and the percentage of mortality in irradiated nematodes was more than that in unirradiated ones. Studying the elemental composition showed that phosphorus (P) was the highest concentration in untreated larvae or those treated with normal S. scapterisci, while calcium (Ca) was the highest concentration in gamma irradiated S. scapterisci. The lowest concentration in untreated larvae was zinc (Zn), while copper (Cu) was the lowest concentration in unirradiated and 2 Gy gamma irradiated S. scapterisci. In conclusion, the gamma irradiation of S. scapteriscienhanced its efficacy against C. pipiens, therefore it could serve as safe control method for C. pipiens.

Optical Emissions Spectroscopy and plume imaging were utilized to investigate flaws generated during directed energy deposition additive manufacturing. Ti-6Al–4 V coupons were built using varying laser power, powder flow rate, and hatching pattern to induce random and systematic flaws. X-Ray Computed Tomography (CT) scans were completed on each part to determine flaw density and flaw locations. Three quantifiable metrics based on the state of the plume during processing are developed, analyzed and compared to the post-build CT scans: (1) line-to-continuum ratio around 430 nm, (2) line-to-continuum ratio around 520 nm, and (3) total plume area. For coupons built with constant laser power, variations in either powder flow rate or hatch pattern that led to an increase in flaw density were accompanied by an increase in median line-to-continuum ratios around 430 and 520 nm and in total plume area. It is also shown that all three metrics appear to measure the same phenomena, i.e. they are internally consistent with each other. These results present a path forward for real-time flaw detection and assessment of build quality in directed energy deposition and powder bed fusion processes.

Process monitoring in additive manufacturing (AM) is a crucial component in the mission of broadening AM industrialization. However, conventional part evaluation and qualification techniques, such as computed tomography (CT), can only be utilized after the build is complete, and thus eliminate any potential to correct defects duringthe build process. In contrast to post-build CT, in situ defect detection based on in situ sensing, such as layerwise visual inspection, enables the potential for in-process re-melting and correction of detected defects and thus facilitates in-process part qualification. This paper describes the development and implementation of such an in situ defect detection strategy for powder bed fusion (PBF) AM using supervised machine learning.

Lack-of-fusion flaws can occur in powder bed fusion (PBF) additive manufacturing of metal components. This paper demonstrates a method for detecting such flaws by monitoring the fabrication of every layer before and after laser scanning with high resolution optical imaging. A binary template is created from the sliced 3D model of the part. Using this template the optical image data is indexed to the part geometry. The indexed image data is used to detect anomalies in the powder layer before laser scanning and in the solidified material after scanning. Lack-of-fusion defects are identified from optical data by correlating multiple images with different lighting conditions and from multiple layers. To test the algorithms intentional defects are created inside a test part at different locations and are successfully detected with a high true positive rate.

•Developed intra-layer, closed-loop control of additive manufacturing build plan.•Control affected macrostructure, microstructure, and mechanical properties.•Demonstrated reduced variability in microstructure and hardness with control.

Through incubation of onion (Allium cepaL.) seed samples on potato dextrose agar and moist filter papers at 28±2°C, 32 species and four varieties belonging to 19 genera of fungi were determined as seedborne in this crop. Among them, 23 species are new reports to the mycoflora of onion seeds.Aspergillus(11 species and three varieties, ∼42.1% of the total colony count of fungi) was the most prevalent genus:A. niger(∼19.7%) was found at the highest rate in the seeds, followed byA. parasiticus(∼17.3%,A. oryzae(∼17.3%) andA. flavus(∼14.5%). The genusAspergilluswas followed byPenicillium(∼12.4%),Sclerotium(∼7.1%),Fennellia, Rhizopus, Chaetomium, DrechsleraandAlternaria(∼12.4%),Fusarium, EmericellaandByssochlamys(∼7.1%), whereas the remaining eight genera displayed a low level of infection (∼10%). Twenty-seven species, three varieties and 12 genera represent Hyphomycetes; four genera, three species and one variety — Ascomycotina; one genus and one species — Zygomycotina; two genera and two species — other Deuteromycotina; and one genus — Oomycetes. Some species, which are known to cause devastating pre- and postharvest diseases to onion crops, were recovered from the seeds of this crop, suggesting the high possibility of their transmission by seed.

One of the challenges for process control of laser powder bed fusion additive manufacturing lies in thermal control. Excessively low laser power may lead to incomplete melting, while too high laser power can lead to keyholing, increasing the porosity of parts. Considering a thermal finite-element model from our prior work, a secant-based iterative method is proposed and implemented in this work to control the simulated laser power to attain a constant melt-pool size. Several experimental samples of Inconel 625 are designed and built with the EOSINT M280 system, and cross-sectioned to evaluate the effectiveness of the iterative simulation-based controller of laser power. Cross-sectional area statistics are collected near laser turnarounds, where the melt pool is most dynamic. The iterative simulation-based controller reduces the variation of melt pool size by between 13.4% and 48.8% compared to applying constant laser power for all configurations. With the extra iterations from the secant method, the controlled simulations take roughly 2.3 times longer than the simulations under constant laser power.

It is widely accepted that gas porosity and lack-of-fusion flaws can degrade both static and dynamic mechanical performance of additively manufactured components, limiting the use of AM components in critical applications. However, the mechanisms by which these flaws form are still debated, and their impact on dynamic material performance have yet to be fully quantified. This work outlines the current understanding of the formation mechanisms for gas porosity, lack-of-fusion, and flaws related to melt pool instabilities. Where possible, the spatial and morphological characteristics of these flaws are highlighted, as well as their impact on mechanical properties. The effect of hot isostatic pressing is also discussed.

Control of laser power to improve part quality is critical for fabrication of complex components via Laser Powder Bed Fusion (LPBF) additive manufacturing (AM) processes. If the laser power is too low, it will result in a small melt pool and lack of fusion; on the other hand, if the laser power is too high, it will result in keyhole and material evaporation. This paper examines a model-based feed-forward control for laser power in LPBF to improve build quality by avoiding the onset of keyhole formation or reducing over-melting. First, an analytical, control-oriented model on the dynamics of melt-pool cross-sectional area in scanning a multi-track part was developed, and then a nonlinear inverse-dynamics controller was designed to adjust laser power such that the melt-pool cross-sectional area can be regulated to a constant set point during the build process. The resulting control trajectory on laser power from the simulated closed-loop controller was then implemented in a LPBF process as a feed-forward (FF) controller for laser power. Multiple bead-on-plate samples of Inconel 625, with different number of tracks and track lengths, were then built on an EOSINT M 280 AM system to evaluate the performance of the resulting FF-Analytic controller. Experimental results demonstrated that the proposed FF-Analytic control of laser power was able to avoid the onset of keyhole formation that occurred under a constant laser power for certain samples. Furthermore, the proposed FF-Analytic control was demonstrated to have significantly reduced over-melting at the returning ends of the laser scan path in scanning a multi-track part compared to applying a constant laser power, albeit with some over-compensation due to modeling imperfection. Overall, the proposed FF-Analytic control of laser power had 23–40% lower average error rate than applying a constant laser power in regulating the melt-pool cross-sectional area to a constant reference value, in terms of measurements of cross-sections at track ends.

The objective of this work is to detect in situthe occurrence of lack-of-fusion defects in titanium alloy (Ti-6Al-4 V) parts made using directed energy deposition (DED) additive manufacturing (AM). We use data from two types of in-process sensors, namely, a spectrometer and an optical camera which are integrated into an Optomec MR-7 DED machine. Both sensors are focused on capturing the dynamic phenomena around the melt pool region. To detect lack-of-fusion defects, we fuse (combine) the data from the in-process sensors invoking the concept of Kronecker product of graphs. Subsequently, we use the features derived from the graph Kronecker product as inputs to a machine learning algorithm to predict the severity (class or level) of average length of lack-of-fusion defects within a layer, which is obtained from offline X-ray computed tomography of the test parts. We demonstrate that the severity of lack-of-fusion defects is classified with statistical fidelity (F-score) close to 85% for a two-level classification scenario, and approximately 70% for a three-level classification scenario. Accordingly, this work demonstrates the use of heterogeneous in-process sensing and online data analytics for in situdetection of defects in DED metal AM process.