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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Tunable Magnetic Labyrinth for Abrikosov Vortices

We study Abrikosov vortex dynamics in a superconducting layer covered with an array of T- and I-shaped magnetically soft permalloy elements. Application of the in-plane field H // polarizes thin permalloy bars forming the array and creates a periodic network of mutually perpendicular lines of the attractive or repulsive magnetic potential for vortices along the bar edges, which can be easily tuned by the rotation of H // . Magneto-optical imaging of the vortex trajectories confirms that such a reconfigurable magnetic potential dominates the vortex motion in a wide temperature range and can be a useful tool for manipulating vortices in fluxonic devices for low-loss microelectronics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Observational Study of the Thermodynamics and Morphological Characteristics of a Midlatitude Continental Cold Pool Event

Abstract Convectively generated cold pools are important to the Earth system as they exert strong controls on deep convective-storm initiation, intensity, and life cycle. Despite their importance, efforts to introduce such cold pool controls into weather and climate models lack guidance and/or physical constraints from cold pool observations. This work presents a detailed, purely observational analysis of a cold pool event that took place on 23–24 May 2011 in north-central Oklahoma. The characteristics of the cold pool, and the spatiotemporal evolution of the hydrometeors and dynamics in the proximity of the cold pool, are studied with high-resolution observations. The unprecedented dataset used in this work to study cold pool characteristics includes an enhanced network of surface weather stations, a high-temporal-frequency sounding array, and the NEXRAD and Atmospheric Radiation Measurement (ARM) Southern Great Plains radar networks. The potential use of NEXRAD surveillance scans to estimate height and propagation speed of the leading edge of the cold pool (LECP) is presented in this work. Manual identification and tracking of the LECP from NEXRAD imagery shows a spatial and temporal heterogeneity of the LECP properties. Surprisingly, over its detected life cycle, the LECP speed remains almost constant, even though the strength of the cold pool diminishes in time and its height varies. Radar analysis shows that pulses of graupel and hail within downdrafts in the convective system generating the cold pool appeared to be related to temporary increases in the LECP height.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Diffusion Codes: Self-Correction from Small(er)-Set Expansion with Tunable Non-locality

Optimal constructions of classical LDPC codes can be obtained by choosing the Tanner graph uniformly at random among biregular graphs. We introduce a class of codes that we call ``diffusion codes'', defined by placing each edge connecting bits and checks on some graph, and acting on that graph with a random SWAP network. By tuning the depth of the SWAP network, we can tune a tradeoff between the amount of randomness -- and hence the optimality of code parameters -- and locality with respect to the underlying graph. For diffusion codes defined on the cycle graph, if the SWAP network has depth $\sim Tn$ with $T> n^{2β}$ for arbitrary $β>0$, then we prove that almost surely the Tanner graph is a lossless ``smaller set'' vertex expander for small sets up size $δ\sim \sqrt T \sim n^β$, with bounded bit and check degree. At the same time, the geometric size of the largest stabilizer is bounded by $\sqrt T$ in graph distance. We argue, based on physical intuition, that this result should hold more generally on arbitrary graphs. By taking hypergraph products of these classical codes we obtain quantum LDPC codes defined on the torus with smaller-set boundary and co-boundary expansion and the same expansion/locality tradeoffs as for the classical codes. These codes are self-correcting and admit single-shot decoding, while having the geometric size of the stabilizer growing as an arbitrarily small power law. Our proof technique establishes mixing of a random SWAP network on small subsystems at times scaling with only the subsystem size, which may be of independent interest.

Combinatorics (math.CO)↗
NASA NTRS1983

The System View

Analysis of spacecraft to ground communications link performance and a description of the Deep Space Network system is provided. Due to the tremendous distances involved in communicating between the spacecraft at the edge of our solar system and Earth, communications link performance is stretched to the limit of theoretical predictions. This is required in order to return the maximum amount of data possible during critical events such as planetary flybys. The link analysis provides a basis for the initial link design before spacecraft launch and performance prediction and monitoring during spacecraft flight. Additionally, it indicates what performance upgrades are required for mission extensions and new missions. Performance is improved through the use of a larger antenna collecting area, greater transmitter power, lower receiving system noise temperature and more sophisticated data coding schemes. The performance of the Deep Space Station configuration which serves as the ground portion of the communications link is assessed.

Noreen, G. K.↗
NASA NTRSDate not supplied

Implementation of a Hybrid Edge Node-Centroid Node Approach for the Generation of Reduced Thermal Models

Reduced thermal models are often required for delivery to organizations that manage observatory or launch models at the highest levels of assembly. However, the effort to generate reduced models, and verify against their detailed counterparts, is a challenge that has not yet been conclusively solved. Higher level organizations often place a limit on the number of nodes for delivered models with the assumption that smaller models generally result in less computation time. However, the burden of producing and verifying the accuracy of the reduced models is placed primarily on the lower-level organizations, which in turn consumes resources needed to produce these models. Limiting the total number of allowable nodes may also prevent users from taking full advantage of software capabilities that allow for faster generation of models, such as finite elements, which generally require more nodes than older centroid based models. A methodology using Thermal Desktop was described in 2010 which used: (1) finite elements and edge nodes for a conduction matrix, (2) centroid nodes for capacitance and radiative computations, and (3) the super network feature to produce a conduction matrix based only on the centroid nodes. At that time, the methodology was clear, but the implementation would have had to be done manually; however, with the inclusion of the OpenTD API, this methodology can now be implemented programmatically and for the first time, be a viable approach for the generation of reduced models. The approach was implemented and developed at the NASA Goddard Space Flight Center (GSFC) for the Capture, Containment, and Return System (CCRS) payload on the Earth Return Orbiter (ERO) as part of the Mars Sample Return (MSR) mission, resulting in the TCYEE tool. ERO features a spacecraft bus provided by Airbus through the European Space Agency (ESA) with node limitations on the delivered CCRS model provided by GSFC. TCYEE was used to generate the reduced model for this delivery and the predictions compared favorably to the detailed model currently in use for the thermal performance evaluation. Furthermore, TCYEE is being explored for potential use on the Roman Space Telescope (RST) for the generation of reduced models for delivery to the launch provider, which also has node limit requirements on the RST observatory model for use in launch simulation analyses. This paper describes the methodology, its implementation, and compares the performance of reduced models generated by TCYEE to their detailed counterparts.

Thermal desktop↗
NASA NTRSDate not supplied

Implementation of a Hybrid Edge Node-Centroid Node Approach for the Generation of Reduced Thermal Models

Reduced thermal models are often required for delivery to organizations that manage observatory or launch models at the highest levels of assembly. However, the effort to generate reduced models, and verify against their detailed counterparts, is a challenge that has not yet been conclusively solved. Higher level organizations often place a limit on the number of nodes for delivered models with the assumption that smaller models generally result in less computation time. However, the burden of producing and verifying the accuracy of the reduced models is placed primarily on the lower-level organizations, which in turn consumes resources needed to produce these models. Limiting the total number of allowable nodes may also prevent users from taking full advantage of software capabilities that allow for faster generation of models, such as finite elements, which generally require more nodes than older centroid based models. A methodology using Thermal Desktop was described in 2010 which used: (1) finite elements and edge nodes for a conduction matrix, (2) centroid nodes for capacitance and radiative computations, and (3) the super network feature to produce a conduction matrix based only on the centroid nodes. At that time, the methodology was clear, but the implementation would have had to be done manually; however, with the inclusion of the OpenTD Application Programming Interface, this methodology can now be implemented programmatically and for the first time, be a viable approach for the generation of reduced models. The approach was implemented and developed at the NASA Goddard Space Flight Center (GSFC) for the Capture, Containment, and Return System (CCRS) payload on the Earth Return Orbiter (ERO) as part of the Mars Sample Return mission, resulting in the TCYEE tool. ERO features a spacecraft bus provided by Airbus through the European Space Agency with node limitations on the delivered CCRS model provided by GSFC. TCYEE was used to generate the reduced model for this delivery and the predictions compared favorably to the detailed model currently in use for the thermal performance evaluation. Furthermore, TCYEE is being explored for potential use on the Roman Space Telescope (RST) for the generation of reduced models for delivery to the launch provider, which also has node limit requirements on the RST observatory model for use in launch simulation analyses. This paper describes the methodology, its implementation, and compares the performance of reduced models generated by TCYEE to their detailed counterparts.

Thermal Desktop↗
DOE OSTI2022

Solid State Power Substations (SSPS): A Multi-Hierarchical Architecture from Substation to Grid Edge

With the growing deployment of distributed generation, or power electronic interfaced renewable energy and storage technologies, the nature and behavior of the grid is changing. Synchronous machine-driven asset contributions to the generation mix are shrinking, leading to concerns regarding grid stability. Furthermore, the scale of smaller distributed PE resources needed for managing the electrical network could dwarf the existing system leading to more complex optimization problems and communication interconnections. This paper introduces the concept of a hierarchal system of controllers that spans the grid edge or the customer end to distribution scale substations or solid-state power substation (SSPS). This concept focuses on minimizing the number of interfaces and optimization considerations in the grid by clustering resources into nodes and hubs. The work validates the concept in a controller hardware-in-the-loop (cHIL) platform.

Chinthavali, Madhu Sudhan↗
DOE OSTI2023

A Medium-/Low-Voltage Joint State Estimator Through Linear Uncertainty Propagation

Traditionally, distribution system state estimations (DSSE) are challenged by the lack of measurements at both primary and secondary sides of the system. The widely available cable television (CATV) voltage sensors installed in low-voltage (LV) networks bring opportunities to achieve higher quality DSSE covering a broader area of the distribution network. This study proposes a medium-/low-voltage (MV/LV) joint distribution system state estimation approach using the untapped CATV measurements. It aims at addressing the need for system situational awareness at the grid edge while improving the estimation accuracy at both the primary and secondary sides compared to its disjointed counterpart. Linearized measurement functions and boundary condition uncertainty propagation rules are derived to ensure the computational efficiency and accuracy of the joint state estimator. Numerical experiments are conducted on an IEEE test feeder to demonstrate the efficacy of the proposed method and the value of CATV measurements.

joint state estimation↗
DOE OSTI2025

Probing Alkylpolyglycoside Hydrogen Bonding and Its Destabilization by Sulfonate Hydrotropes with X‑ray and Vibrational Spectroscopy

It has been proposed that intersurfactant H-bonding networks can produce highly stable foams, specifically those generated from solutions containing alkylpolyglycoside surfactants. In this work, we aim to characterize the presence and destabilization of these networks by introducing a hydrotrope (sodium p-toluene sulfate) at various concentrations into an alkylpolyglycoside (Glucopon 225 DK) surfactant containing solution. Solution surface properties are probed by using aerosol velocity map imaging X-ray photoelectron spectroscopy (A-VMI-XPS) and tensiometer measurements. Bulk properties below the surface are probed using Fourier transform infrared spectroscopy (FTIR) and C-edge near-edge X-ray fine structure spectroscopy (NEXAFS). The surface measurements provide the framework to describe the destabilization of the solution H-bonding network, while the bulk solution measurements provide hints about the disruption of the hydrogen bonding network upon hydrotrope addition. The collected data support the hypothesis that the destabilization of the intersurfactant H-bonding network frees surfactant molecules from the bulk, increasing surfactant population at the air/water interface. This was quantified through an increase in peak area and width in XPS measurements as well as a decrease in surfactant critical micelle concentration. Three regimes with increasing amounts of hydrotrope addition are suggested, described as (1) a hydrotrope affecting only surfactant surface properties, (2) a hydrotrope affecting surfactant surface and bulk properties, and (3) hydrotrope-dominated surface and bulk properties. Future studies will characterize foam stability across the hydrotrope concentration regimes to better define correlations between intersurfactant H-bonding networks and foam stability.

Molecules↗
DOE OSTI2022

Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks

We use graph convolutional neural networks (GCNNs) to produce fast and accurate predictions of the total energy of solid solution binary alloys. GCNNs allow us to abstract the lattice structure of a solid material as a graph, whereby atoms are modeled as nodes and metallic bonds as edges. This representation naturally incorporates information about the structure of the material, thereby eliminating the need for computationally expensive data pre-processing which would be required with standard neural network (NN) approaches. We train GCNNs on ab-initio density functional theory (DFT) for copper-gold (CuAu) and iron-platinum (FePt) data that has been generated by running the LSMS-3 code, which implements a locally self-consistent multiple scattering method, on OLCF supercomputers Titan and Summit. GCNN outperforms the ab-initio DFT simulation by orders of magnitude in terms of computational time to produce the estimate of the total energy for a given atomic configuration of the lattice structure. We compare the predictive performance of GCNN models against a standard NN such as dense feedforward multi-layer perceptron (MLP) by using the root-mean-squared errors to quantify the predictive quality of the deep learning (DL) models. We find that the attainable accuracy of GCNNs is at least an order of magnitude better than that of the MLP.

Lupo Pasini, Massimiliano↗
DOE OSTI2021

Inference of Gene Regulatory Network Uncovers the Linkage between Circadian Clock and Crassulacean Acid Metabolism in Kalanchoë fedtschenkoi

The circadian clock drives time-specific gene expression, enabling biological processes to be temporally controlled. Plants that conduct crassulacean acid metabolism (CAM) photosynthesis represent an interesting case of circadian regulation of gene expression as stomatal movement is temporally inverted relative to stomatal movement in C3 plants. The mechanisms behind how the circadian clock enabled physiological differences at the molecular level is not well understood. Recently, the rescheduling of gene expression was reported as a mechanism to explain how CAM evolved from C3. Therefore, we investigated whether core circadian clock genes in CAM plants were re-phased during evolution, or whether networks of phase-specific genes were simply re-wired to different core clock genes. We identified candidate core clock genes based on gene expression features and then applied the Local Edge Machine (LEM) algorithm to infer regulatory relationships between this new set of core candidates and known core clock genes in Kalanchoë fedtschenkoi. We further inferred stomata-related gene targets for known and candidate core clock genes and constructed a gene regulatory network for core clock and stomata-related genes. Our results provide new insight into the mechanism of circadian control of CAM-related genes in K. fedtschenkoi, facilitating the engineering of CAM machinery into non-CAM plants for sustainable crop production in water-limited environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Multi-Level Structural Damage Characterization Using Sparse Acoustic Sensor Networks and Knowledge Transferred Deep Learning

Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50% during localization and 24% during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100% prediction accuracy at all levels of damage characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Short- to Intermediate-Range Structure, Transport, and Thermophysical Properties of LiF–NaF–ZrF4 Molten Salts

LiF–NaF–ZrF 4 multicomponent molten salts are identified as promising candidates for coolant salts in molten salt reactors and advanced high-temperature reactors. This study focused on low-melting point salt compositions of interest: 38LiF–51NaF–11ZrF 4 , 42LiF–29NaF–29ZrF 4 , and 26LiF–37NaF–37ZrF 4 . Ab-initio molecular dynamics (AIMD) calculations were performed and compared with available experimental data to assess the ability of rigid ion models (RIM) to reproduce short- to intermediate-range structure, transport, and thermophysical properties of the LiF–NaF–ZrF 4 salt mixtures. It is found that as ZrF 4 mol% increases, the average cation–anion coordination number (CN) of monovalent cations (Li + , Na + ) obtained from RIM calculations decreases, while multivalent Zr 4+ CN varied from 15% to 19% in comparison to corresponding AIMD values. In addition, RIM is found to predict the existence of 7, 8, and 9 coordinated fluorozirconate complexes, while AIMD and the available experimental data showed an occurrence of 6, 7, and 8 coordinated complexes in the melt. The intermediate-range structure analysis revealed that while the RIM parameters are able to reproduce a local structure for lower ZrF 4 mol% salts such as in 38LiF–51NaF–11ZrF 4 , an extensive fluorozirconate network formation is observed in RIM simulations for higher ZrF 4 mol% compositions. The network generated by RIM parameters is found to be mainly connected by “corner-sharing” fluorozirconate complexes as opposed to both “edge-sharing” and “corner-sharing” connectively portrayed by AIMD. It is found that a close agreement between AIMD and the RIM salt structure for the 11-mol% ZrF 4 salt resulted in good agreement in the calculated Zr diffusivities and the viscosity values. However, due to the inaccurate short- to intermediate-range structure prediction by RIM for higher ZrF 4 mol% compositions, thermophysical properties such as densities and heat capacity differ by up to 26% and 27%, respectively, upon comparison with AIMD and experimental values. Also, the network-dominated properties such as diffusion coefficients and viscosities differed by up to two and three orders of magnitude, respectively. This study signifies the importance of accurate salt structure generation for an accurate prediction of transport and thermophysical properties of multicomponent molten salts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Design and Challenges of Edge Computing ASICs on Front-End Electronics

In situ or hardware-embedded data processing of raw signals, close to their source, in radiation detectors is expected to provide dramatic improvements in data quality and volumes. However, the implementation of artificial neural networks (ANNs) in the front-end electronics, and the design of custom integrated circuits (ASICs), comes with challenges. In addition, detectors have to operate with limited power budget and implement complex functionalities in a very dense space. They often are exposed to extreme conditions as they work in high-radiation environments and/or cryogenic temperatures. This paper presents examples of applications and design methodologies for in-situ ANNs, along with the challenges of retaining the fidelity of the trained networks. For illustration, we use the problem of estimating the energy deposited by the radiation from digitized waveforms. The proposed implementation starts with an ML algorithm trained in Qkeras and eventually leads to an equivalent ASIC implementation. The associated design challenges in realizing energy and area efficient implementations in CMOS processes are reviewed. Novel approaches that employ hybrid technologies (combination of CMOS with memristors), in-memory computing models, and bio-inspired spiking neural networks are also highlighted

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure --- this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗
DOE OSTI2022

SPATIAL GRAPH ATTENTION AND CURIOSITY-DRIVEN POLICY FOR ANTIVIRAL DRUG DISCOVERY

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure - this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Y↗
DOE OSTI2025

Electronic Configurational Transformation of Network Modifiers in Aluminate Glass above Megabar Pressures

Electronic responses of glasses under extreme pressures differ from those of crystalline analogs. Their distinct electronic environments are found in network formers with well-defined, covalent-bonded coordination environments (e.g., [4] Si and [4] Al) and in network modifiers with more disordered, ionic-bonded configurations (e.g., [5,6,7] Ca). Deciphering the evolution of the bonding environment of network modifier cations upon compression provides atomic insights into the pressure-driven hardening and transport properties of glasses. Despite the importance, in contrast to extensive efforts to uncover how network formers behave under pressure, considerable structural disorder around network modifiers makes it challenging to probe their electronic bonding environments under compression. Our understanding of the evolution of network modifiers above megabars is currently absent. Here, we report a discovery of highly densified electronic configurations of network modifier Ca in aluminate glass under extreme compression via the first inelastic X-ray scattering at the Ca L-edge up to 140 GPa. As evidenced by the prominent pressure-driven increases in electronic dispersion and delocalization, densified calcium environments are characterized by a decreased average Ca–O distance, the formation of highly coordinated calcium, a broader distribution of topological variables, and a greater distortion of Ca polyhedra above megabars. The spectral features for the Ca environments reveal significant electronic and bonding modifications, including pressure-driven increases in the ligand field interaction, the covalence characteristic of the Ca–O bond, and the electron–hole Coulomb interaction. These densification paths identify the electronic adaptation of network modifiers above megabars, shedding light on the origins of enhanced electron transport and the electron-storing capacity of glasses under pressure.

amorphous materials↗
NASA NTRS2010

Sun-Earth Day: Reaching the Education Audience by Informal Means

For ten years the Sun-Earth Day program has promoted Heliophysics education to ever larger audiences through events centered on attractive annual themes. What originally started out as a one day event quickly evolved into a series of programs and events that occur throughout the year culminating with a celebration on or near the Spring Equinox. The events are often formal broadcasts or webcasts seeking to convey the science behind the latest solar-terrestrial mission discoveries. This has been quite successful, but it is clear that the younger generation increasingly depends on social networking approaches and informal news transmission for learning what is happening in the world around them. For 2010, the Sun-Earth Day team put emphasis on using informal approaches to bring the theme to the audience. The main event, a webcast from the NASA booth at the National Science Teachers Association (NSTA) annual meeting by the NASA EDGE group, took a lighthearted and offbeat approach to interviewing scientists and educators about Heliophysics news. NASA EDGE programs are unscripted and unpredictable, and that represents a different approach to getting the message across. The webcast was supplemented by a number of social networking avenues. The Sun-Earth Day program explored a wide range of social media applications including Facebook, Twitter, NING, podcasting, iPhone apps, etc. Each of these offers unique and effective methods to promote Heliophysics content and mission related highlights. The facebook site was quite popular and message posting there told the Sun-Earth Day story piece by piece. The same could be said of twittering and the tweetup held at the NSTA site. Has all of this been effective? Results are still being gathered, but anecdotal responses from the world seem very positive. What other methods might be used in the future to bring the science to a personal hands-on, interactive experience? Outcomes: Participants will: (1) Be introduced to the Sun-Earth Day program and its evolution through a decade of programs; (2) Hear about the methods used to communicate and educate through the years and how well they have worked; and (3) Be acquainted with the latest usage of social networking and informal education approaches and how well they have worked

Thieman, J.↗