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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Model Fit to Experimental Data for Foam-Assisted Deep Vadose Zone Remediation

Foam has been regarded as a promising means of remeidal amendment delivery to overcome subsurface heterogeneity in subsurface remediation processes. This study investigates how a foam model, developed by Method of Characteristics and fractional flow analysis in the companion paper of Roostapour and Kam (2012), can be applied to make a fit to a set of existing laboratory flow experiments (Zhong et al., 2009) in an application relevant to deep vadose zone remediation. This study reveals a few important insights regarding foam-assisted deep vadose zone remediation: (i) the mathematical framework established for foam modeling can fit typical flow experiments matching wave velocities, saturation history , and pressure responses; (ii) the set of input parameters may not be unique for the fit, and therefore conducting experiments to measure basic model parameters related to relative permeability, initial and residual saturations, surfactant adsorption and so on should not be overlooked; and (iii) gas compressibility plays an important role for data analysis, thus should be handled carefully in laboratory flow experiments. Foam kinetics, causing foam texture to reach its steady-state value slowly, may impose additional complications.

Roostapour, A.↗
DOE OSTI2019

Lu-Hf isotopic evidence of a deep mantle plume source for the ~2.06 Ga Bushveld Large Igneous Province

The Bushveld Large Igneous Province (B-LIP) comprises a diverse array of >30 magma bodies that intruded the Kaapvaal Craton at ~2.06 Ga. Here, we use zircon and bulk-rock Lu-Hf isotope data to determine whether the B-LIP formed in response to the arrival of a plume(s) from the deep mantle or by melting of the depleted upper mantle during foundering of an eclogitized residue at the base of the lithosphere. New zircon Hf isotope compositions for four B-LIP bodies yield intrusion-specific average εHf (2.06Ga) values that range from -20.7 ± 2.8 to -2.7 ± 2.8, largely consistent with literature zircon data for other B-LIP intrusions. Bulk-rock solution εHf (2.06Ga) values for a variety of B-LIP intrusions range from -2.1 ± 0.2 to -10.6 ± 0.2. Because the most radiogenic Hf isotope compositions across the entire B-LIP are nearly primordial, having an εHf (2.06Ga) close to 0, it is likely that the heat source of the B-LIP was a plume(s) from deep mantle. The Hf isotope data further indicates that individual intrusions in the B-LIP were produced by melting of three distinct source reservoirs (in addition to melts derived from the plume itself): 1) Subduction and plume modified continental lithospheric mantle; 2) Older (~2.7 Ga) mafic-ultramafic plume-related material trapped in the Kaapvaal lithosphere; and 3) Mid- to upper Kaapvaal crust. The presence of ~2.7 Ga mafic-ultramafic material in the Kaapvaal lithosphere may have acted to strengthen the lithosphere so that it was able to resist being dispersed by the arrival of the B-LIP plume at ~2.06 Ga.

58 GEOSCIENCES↗
DOE OSTI2023

Hydrate-bearing sands in the Terrebonne Basin record the transition from ponded deposition to bypass in the deep-water Gulf of Mexico

Here, we integrated well and seismic data to interpret the stratigraphic architecture and the paleogeographic evolution of two hydrate-bearing sand reservoirs in the Terrebonne Basin in the deep-water Gulf of Mexico. These sands were deposited during the final stage of ponded fill within the Terrebonne Basin. The Green sand filled the final accommodation in the basin as a perched apron; it was formed coincidentally with downstream erosion and sediment bypass which cut through a canyon formed on the southeastern edge of the basin. Subsequently, a low energy turbidite channel continued to bypass sediment through the basin and aggrade. The Orange sand was then deposited as a second perched apron and it was once again incised by the low energy turbidite channel. The Green and Orange sands are regional sheet sands that extend and thicken deep into the Terrebonne Basin. They provide a connected regional aquifer that captured and focused gas migration to the crest of the structure where hydrate was formed. These sands were formed by large, unconfined, turbidity flows. We envision that they will be composed of coarser grained deposits relative to previously explored hydrate reservoirs within levee deposits. As such, they have the potential to be relatively coarse grained and hence have higher permeability: a condition favorable to production of hydrate deposits.

3D seismic interpretation↗
DOE OSTI2018

Rock-welding materials development for deep borehole nuclear waste disposal

We report that various versions of deep borehole nuclear waste disposal have been proposed in the past in which effective sealing of a borehole after waste emplacement is generally required. In a high temperature disposal mode, the sealing function will be fulfilled by melting the ambient granitic rock with waste decay heat or an external heating source, creating a melt that will encapsulate waste containers or plug a portion of the borehole above a stack of the containers. However, there are certain drawbacks associated with natural materials, such as high melting temperatures, inefficient consolidation, slow crystallization kinetics, the resulting sealing materials generally being porous with low mechanical strength, insufficient adhesion to waste container surface, and lack of flexibility for engineering controls. In this study, we showed that natural granitic materials can be purposefully engineered through chemical modifications to enhance the sealing capability of the materials for deep borehole disposal. The present work systematically explores the effect of chemical modification and crystallinity (amorphous vs. crystalline) on the melting and crystallization processes of a granitic rock system. The approach can be applied to modify granites excavated from different geological sites. Several engineered granitic materials have been explored which possess significantly lower processing and densification temperatures than natural granites. Finally, those new materials consolidate more efficiently by viscous flow and accelerated recrystallization without compromising their mechanical integrity and properties.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Microwave heating synthesis and formation mechanism of chalcopyrite structured CuInS{sub 2} nanorods in deep eutectic solvent

Graphical abstract: Chalcopyrite structured CuInS{sub 2} nanorods were synthesized by an environmentally friendly microwave heating method in deep eutectic solvent. Results show that microwave heating time plays an important role in the formation of CuInS{sub 2} nanostructure phase. The SEM results indicated that the obtained CuInS{sub 2} nanostructures display rod-like morphology with diameters of about 40 nm and lengths of about 400 nm. The UV–vis spectrum results indicated that the CuInS{sub 2} nanorods exhibit strong absorption from the entire visible light region to the near-infrared region beyond 1100 nm. The possible growth mechanism of CuInS{sub 2} nanorods was discussed. - Abstract: Chalcopyrite structured CuInS{sub 2} nanorods were synthesized by an environmentally friendly microwave heating method in deep eutectic solvent. The as-synthesized CuInS{sub 2} nanorods were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS), respectively. The results indicated that the obtained CuInS{sub 2} nanostructures display rod-like morphology with diameters of about 40 nm and lengths of about 400 nm. The influences of microwave heating time on the formation of CuInS{sub 2} phase were discussed. Ultraviolet–visible (UV–vis) and photoluminescence (PL) spectra were utilized to investigate the optical properties of CuInS{sub 2} nanorods. The results showed that the as-synthesized CuInS{sub 2} nanorods exhibit strong absorption from the entire visible light region to the near-infrared region beyond 1100 nm. PL spectrum of the as-synthesized CuInS{sub 2} nanorods displays an emission peak centered at 580 nm under excitation wavelength of 366 nm at room temperature. The possible growth mechanism of CuInS{sub 2} nanorods was discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness

We report the accuracy of deep learning, i.e., deep neural networks, can be characterized by dividing the total error into three main types: approximation error, optimization error, and generalization error. Whereas there are some satisfactory answers to the problems of approximation and optimization, much less is known about the theory of generalization. Most existing theoretical works for generalization fail to explain the performance of neural networks in practice. To derive a meaningful bound, we study the generalization error of neural networks for classification problems in terms of data distribution and neural network smoothness. We introduce the cover complexity (CC) to measure the difficulty of learning a data set and the inverse of the modulus of continuity to quantify neural network smoothness. A quantitative bound for expected accuracy/error is derived by considering both the CC and neural network smoothness. Although most of the analysis is general and not specific to neural networks, we validate our theoretical assumptions and results numerically for neural networks by several data sets of images. The numerical results confirm that the expected error of trained networks scaled with the square root of the number of classes has a linear relationship with respect to the CC. We also observe a clear consistency between test loss and neural network smoothness during the training process. In addition, we demonstrate empirically that the neural network smoothness decreases when the network size increases whereas the smoothness is insensitive to training dataset size.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

A scaler-based data acquisition system for measuring parity-violating asymmetry in deep inelastic scattering

An experiment that measured the parity violating asymmetries in deep inelastic scattering was completed at the Thomas Jefferson National Accelerator Facility in experimental Hall A. From these asymmetries, a combination of the quark weak axial charge could be extracted with a factor of five improvement in precision over world data. To achieve this, asymmetries at the 10^-4 level needed to be measured at event rates up to 500 kHz and the high pion background typical to deep inelastic scattering experiments needed to be rejected efficiently. A specialized data acquisition (DAQ) system with intrinsic particle identification (PID) was successfully developed and used: The pion contamination in the electron samples was controlled at the order of 2 × 10^-4 or below with an electron efficiency of higher than 91% throughout the production period of the experiment, the systematic uncertainty in the measured asymmetry due to DAQ deadtime was below 0.2%, and the statistical quality of the asymmetry measurement agreed with the Gaussian distribution to over five orders of magnitudes. The DAQ system is presented here with an emphasis on its design scheme, the achieved PID performance, deadtime effect and the capability of measuring small asymmetries.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Numerical simulation of vortex-induced motion of a deep-draft paired-column semi-submersible offshore platform

Understanding and predicting vortex-induced motion (VIM) of offshore systems for deep seawater applications is crucial to improve the system safety and integrity. In this paper, we report on experimental tow-tank measurements and numerical simulations of VIM of a deep-draft offshore platform, specifically Paired-Column Semisubmersible (PC-Semi). The study is carried out in model scale (1:54), at subcritical flow regime with Re~10 4 . Motion of the floating structure has three degrees of freedom: in-line, cross-flow, and yaw. Large periodic cross-flow motion is measured for headings 0°, 11.25°, and 22.5°, for reduced velocities (U r ) between 5 and 10. Considerably smaller cross-flow amplitude is recorded at 45° heading across the U r range considered. An extensive sensitivity study is performed using computational fluid dynamics (CFD) to capture the transient displacement history of VIM (in-line, cross-flow, and yaw motion components). Amplitude and period of cross-flow (transverse) motion are obtained from statistical analysis of VIM time history and subsequently used as the validation criterion between the CFD simulation and the model tests. Satisfactory agreement between the CFD results and tow-tank measurements is achieved with a Delayed Detached Eddy Simulation – Shear Stress Transport (DDES-SST) formulation. Finally, this work provides experimental results and serves as a practical starting point to set up a CFD problem to estimate amplitude and period of cross-flow VIM motion for offshore engineering applications.

42 ENGINEERING↗
DOE OSTI2017

Anisotropy in the deep Earth

Not provided.Seismic anisotropy has been found in many regions of the Earth's interior. Its presence in the Earth's crust has been known since the 19th century, and is due in part to the alignment of anisotropic crystals in rocks, and in part to patterns in the distribution of fractures and pores. In the upper mantle, seismic anisotropy was discovered 50 years ago, and can be attributed for the most part, to the alignment of intrinsically anisotropic olivine crystals during large scale deformation associated with convection. There is some indication for anisotropy in the transition zone, particularly in the vicinity of subducted slabs. Here we focus on the deep Earth – the lower mantle and core, where anisotropy is not yet mapped in detail, nor is there consensus on its origin. Most of the lower mantle appears largely isotropic, except in the last 200–300 km, in the D" region, where evidence for seismic anisotropy has been accumulating since the late 1980s, mostly from shear wave splitting measurements. Recently, a picture has been emerging, where strong anisotropy is associated with high shear velocities at the edges of the large low shear velocity provinces (LLSVPs) in the central Pacific and under Africa. These observations are consistent with being due to the presence of highly anisotropic MgSiO3 post-perovskite crystals, aligned during the deformation of slabs impinging on the core-mantle boundary, and upwelling flow within the LLSVPs. In this report we also discuss mineral physics aspects such as ultrahigh pressure deformation experiments, first principles calculations to obtain information about elastic properties, and derivation of dislocation activity based on bonding characteristics. Polycrystal plasticity simulations can predict anisotropy but models are still highly idealized and neglect the complex microstructure of polyphase aggregates with strong and weak components. A promising direction for future progress in understanding the origin of seismic anisotropy in the deep mantle and its relation to global mantle circulation, is to link macroscopic information from seismology and microscopic information mineral physics through geodynamics modeling. Anisotropy in the inner core was proposed 30 years ago to explain faster P wave propagation along the direction of the Earth's axis of rotation as well as anomalous splitting of core sensitive free oscillations. There is still uncertainty about the origin of this anisotropy. In particular, it is difficult to explain its strength, based on known elastic properties of iron, as it would require almost perfect alignment of iron crystals. Indeed, the strongly anomalous P travel times observed on paths from the South Sandwich Islands to Alaska may or may not be due to inner core anisotropy, and will need to be explained before consensus can be reached on the strength of anisotropy in the inner core and its origin.

58 GEOSCIENCES↗
DOE OSTI2017

A novel pathogenic aviadenovirus from red-bellied parrots (Poicephalus rufiventris) unveils deep recombination events among avian host lineages

Competing roles of coevolution, selective pressure and recombination are an emerging interest in virus evolution. We report a novel aviadenovirus from captive red-bellied parrots (Poicephalus rufiventris) that uncovers evidence of deep recombination among aviadenoviruses. The sequence identity of the virus was most closely related to Turkey adenovirus D (42% similarity) and other adenoviruses in chickens, turkeys and pigeons. Sequencing and comparative analysis showed that the genome comprised 40,930 nucleotides containing 42 predicted open reading frames (ORFs) 19 of which had strong similarity with genes from other adenovirus species. The new genome unveiled a lineage that likely participated in deep recombination events across the genus Aviadenovirus accounting for an ancient evolutionary relationship. We hypothesize frequent host switch events and recombination among adenovirus progenitors in Galloanserae hosts caused the radiation of extant aviadenoviruses and the newly assembled Poicephalus adenovirus genome points to a potentially broader host range of these viruses among birds. - Highlights: •Shows how a single new genome can change overall phylogeny. •Reveals host switch events among adenovirus progenitors in Galloanserae hosts. •Points to a potentially broader host range of adenoviruses among birds and wildlife .

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Drop impact into a deep pool: vortex shedding and jet formation

One of the simplest splashing scenarios results from the impact of a single drop on a deep pool. The traditional understanding of this process is that the impact generates an axisymmetric sheet-like jet that later breaks up into secondary droplets. Recently it was shown that even this simplest of scenarios is more complicated than expected because multiple jets can be generated from a single impact event and there are transitions in the multiplicity of jets as the experimental parameters are varied. Here, we use experiments and numerical simulations of a single drop impacting on a deep pool to examine the transition from impacts that produce a single jet to those that produce two jets. Using high-speed X-ray imaging methods we show that vortex separation within the drop leads to the formation of a second jet long after the formation of the ejecta sheet. Using numerical simulations we develop a phase diagram for this transition and show that the capillary number is the most appropriate order parameter for the transition.

breakup/coalescence↗
DOE OSTI2023

Deep learning closure models for large-eddy simulation of flows around bluff bodies

Near-wall flow simulation remains a central challenge in aerodynamics modelling: Reynolds-averaged Navier–Stokes predictions of separated flows are often inaccurate, and large-eddy simulation (LES) can require prohibitively small near-wall mesh sizes. A deep learning (DL) closure model for LES is developed by introducing untrained neural networks into the governing equations and training in situ for incompressible flows around rectangular prisms at moderate Reynolds numbers. The DL-LES models are trained using adjoint partial differential equation (PDE) optimization methods to match, as closely as possible, direct numerical simulation (DNS) data. They are then evaluated out-of-sample – for aspect ratios, Reynolds numbers and bluff-body geometries not included in the training data – and compared with standard LES models. The DL-LES models outperform these models and are able to achieve accurate LES predictions on a relatively coarse mesh (downsampled from the DNS mesh by factors of four or eight in each Cartesian direction). We study the accuracy of the DL-LES model for predicting the drag coefficient, near-wall and far-field mean flow, and resolved Reynolds stress. A crucial challenge is that the LES quantities of interest are the steady-state flow statistics; for example, a time-averaged velocity component $\langle {u}_i\rangle (x) = \lim _{t \rightarrow \infty } ({1}/{t}) \int _0^t u_i(s,x)\, {\rm d}s$ . Calculating the steady-state flow statistics therefore requires simulating the DL-LES equations over a large number of flow times through the domain. It is a non-trivial question whether an unsteady PDE model with a functional form defined by a deep neural network can remain stable and accurate on $t \in [0, \infty )$ , especially when trained over comparatively short time intervals. Our results demonstrate that the DL-LES models are accurate and stable over long time horizons, which enables the estimation of the steady-state mean velocity, fluctuations and drag coefficient of turbulent flows around bluff bodies relevant to aerodynamics applications.

Mechanics↗
DOE OSTI2023

Deep learning for morphological identification of extended radio galaxies using weak labels

Abstract The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple components. The algorithm is trained on weak class-level labels of radio galaxies to get class activation maps (CAMs). The CAMs are further refined using an inter-pixel relations network (IRNet) to get instance segmentation masks over radio galaxies and the positions of their infrared hosts. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe (EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS sensitivity of 25–35 $\mu$ Jy beam $^{-1}$ . We demonstrate that weakly-supervised deep learning algorithms can achieve high accuracy in predicting pixel-level information, including masks for the extended radio emission encapsulating all galaxy components and the positions of the infrared host galaxies. We evaluate the performance of our method using mean Average Precision (mAP) across multiple classes at a standard intersection over union (IoU) threshold of 0.5. We show that the model achieves a mAP $_{50}$ of 67.5% and 76.8% for radio masks and infrared host positions, respectively. The network architecture can be found at the following link: https://github.com/Nikhel1/Gal-CAM

Astronomy & Astrophysics↗
DOE OSTI2024

Coupling Microdroplet-Based Sample Preparation, Multiplexed Isobaric Labeling, and Nanoflow Peptide Fractionation for Deep Proteome Profiling of the Tissue Microenvironment

There is increasing interest in developing in-depth proteomic approaches for mapping tissue heterogeneity in a cell-type-specific manner to better understand and predict the function of complex biological systems such as human organs. Existing spatially resolved proteomics technologies cannot provide deep proteome coverage due to limited sensitivity and poor sample recovery. Herein, we seamlessly combined laser capture microdissection with a low-volume sample processing technology that includes a microfluidic device named microPOTS (microdroplet processing in one pot for trace samples), multiplexed isobaric labeling, and a nanoflow peptide fractionation approach. The integrated workflow allowed us to maximize proteome coverage of laser-isolated tissue samples containing nanogram levels of proteins. We demonstrated that the deep spatial proteomics platform can quantify more than 5000 unique proteins from a small-sized human pancreatic tissue pixel (∼60,000 μm2) and differentiate unique protein abundance patterns in pancreas. Furthermore, the use of the microPOTS chip eliminated the requirement for advanced microfabrication capabilities and specialized nanoliter liquid handling equipment, making it more accessible to proteomic laboratories.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. In this work, larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Toward Stable Deep-Blue Luminescent Colloidal Lead Halide Perovskite Nanoplatelets: Systematic Photostability Investigation

Colloidal lead halide perovskite nanocrystals and nanoplatelets have emerged as promising semiconductor nanomaterials because of their spectral tunability, facile processability, and bright emission with high color purity. In particular, strong quantum and dielectric confinement make atomically thin colloidal lead bromide perovskite nanoplatelets a favorable candidate for next-generation deep-blue-emitting (λ max = 437 nm) materials. However, poor photostability poses a critical challenge; colloidal nanoplatelets suffer from photobleaching or transformation into thicker, less-confined nanostructures with red-shifted emission upon UV irradiation. In this study, we synthesize deep-blue-emitting organic–inorganic hybrid perovskite nanoplatelets (formula: L 2 [ABX 3 ]BX 4 , L: butylammonium and octylammonium, A: methylammonium or formamidinium, B: lead, and X: bromide or iodide) with large lateral dimension (~1 μm) by ligand-assisted reprecipitation and systematically investigate the factors that affect the photostability of those nanoplatelets. We find that freshness of the prepared precursor solutions for ligand-assisted reprecipitation is critical to obtain better stability with high photoluminescence quantum yield of perovskite nanoplatelets. Photobleaching is found to result from intrinsic instability of the perovskite lattice against UV irradiation in nanoplatelets, whereas transformation into thicker nanostructures results from extrinsic factors—moisture, primarily. Furthermore, we observe that substitution of the organic cation from formamidinium to methylammonium and addition of excess alkylammonium bromide ligands significantly enhance both the ambient and photostability. Lastly, we demonstrate that the dropcast film of methylammonium lead bromide nanoplatelets with excess alkylammonium bromide ligands shows dramatically improved stability both under UV irradiation and under ambient conditions. Lastly, this study expands our understanding of the factors that affect perovskite nanoplatelet photostability and opens up new possibilities for the fabrication of stable perovskite nanoplatelet-based optoelectronic devices.

2D↗
DOE OSTI2021

Surfactants are Ineffective for Reducing Imbibition of Water-Based Fracturing Fluids in Deep Gas Reservoirs

Minimizing loss of injected hydraulic fracturing fluids into shale along fracture-matrix boundaries is desired because imbibed water restricts gas production and wastes valuable water resources. This problem has motivated the addition of surfactants into water-based hydraulic fracturing fluids in order to reduce the capillary driving force for imbibition. Here, we show that reduction in interfacial tension and wettability alteration has negligible ability to reduce imbibition in deep gas reservoirs. The effectiveness of altering capillary forces acting at the wetting front also depends on the injection pressure acting at the fracture-matrix boundary. The pressure at the interface between the fracture and the shale matrix is constrained between the reservoir pore pressure and formation pressure (rock fracture pressure, also known as breakdown pressure of the rock) and increases with depth to magnitudes that greatly exceed that of capillary pressures. Furthermore, the analyses presented here show that even maximum alteration of interfacial properties that result in strongly hydrophobic interactions between the fracturing fluid and reservoir rock is incapable of significantly reducing imbibition in deep reservoirs. Instead of using surfactants, this analysis points to decreases in wellbore shut-in pressures and shut-in times as practical options for reducing imbibition losses of water-based fluids.

03 NATURAL GAS↗
DOE OSTI2021

Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists

The α2a adrenoceptor is a particularly medically relevant subtype of the G protein-couple receptor family. Unfortunately, high-throughput techniques aimed at producing novel drug leads targeted to this receptor have been largely unsuccessful due to the complex pharmacology of adrenergic receptors. As such, cutting-edge in silico ligand- and structure-based assessment and de novo deep learning methods are well-positioned to provide new insights into protein-ligand interactions and potential active compounds. In this work, we (i) collect a dataset of α2a adrenoceptor agonists and provide it as a resource for the drug design community; (ii) use the dataset as a basis to generate candidate active structures via deep learning; and (iii) apply computational ligand- and structure-based analysis techniques to gain new insight into α2a adrenoceptor agonists and assess the quality of the computer-generated compounds. Additionally, we further describe how such assessment techniques can be applied to putative chemical probes with a case study involved proposed medetomidine-based probes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗