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At least 1,063 records · Page 59

Drug discovery efforts at George Mason University

With over 39,000 students, and research expenditures in excess of $200 million, George Mason University (GMU) is the largest R1 (Carnegie Classification of very high research activity) university in Virginia. Mason scientists have been involved in the discovery and development of novel diagnostics and therapeutics in areas as diverse as infectious diseases and cancer. Below are highlights of the efforts being led by Mason researchers in the drug discovery arena. To enable targeted cellular delivery, and non-biomedical applications, Veneziano and colleagues have developed a synthesis strategy that enables the design of self-assembling DNA nanoparticles (DNA origami) with prescribed shape and size in the 10 to 100 nm range. The nanoparticles can be loaded with molecules of interest such as drugs, proteins and peptides, and are a promising new addition to the drug delivery platforms currently in use. The investigators also recently used the DNA origami nanoparticles to fine tune the spatial presentation of immunogens to study the impact on B cell activation. These studies are an important step towards the rational design of vaccines for a variety of infectious agents. To elucidate the parameters for optimizing the delivery efficiency of lipid nanoparticles (LNPs), Buschmann, Paige and colleagues have devised methods for predicting and experimentally validating the pKa of LNPs based on the structure of the ionizable lipids used to formulate the LNPs. These studies may pave the way for the development of new LNP delivery vehicles that have reduced systemic distribution and improved endosomal release of their cargo post administration. To better understand protein-protein interactions and identify potential drug targets that disrupt such interactions, Luchini and colleagues have developed a methodology that identifies contact points between proteins using small molecule dyes. The dye molecules noncovalently bind to the accessible surfaces of a protein complex with very high affinity, but are excluded from contact regions. When the complex is denatured and digested with trypsin, the exposed regions covered by the dye do not get cleaved by the enzyme, whereas the contact points are digested. The resulting fragments can then be identified using mass spectrometry. The data generated can serve as the basis for designing small molecules and peptides that can disrupt the formation of protein complexes involved in disease processes. For example, using peptides based on the interleukin 1 receptor accessory protein (IL-1RAcP), Luchini, Liotta, Paige and colleagues disrupted the formation of IL-1/IL-R/IL-1RAcP complex and demonstrated that the inhibition of complex formation reduced the inflammatory response to IL-1B. Working on the discovery of novel antimicrobial agents, Bishop, van Hoek and colleagues have discovered a number of antimicrobial peptides from reptiles and other species. DRGN-1, is a synthetic peptide based on a histone H1-derived peptide that they had identified from Komodo Dragon plasma. DRGN-1 was shown to disrupt bacterial biofilms and promote wound healing in an animal model. The peptide, along with others, is being developed and tested in preclinical studies. Other research by van Hoek and colleagues focuses on in silico antimicrobial peptide discovery, screening of small molecules for antibacterial properties, as well as assessment of diffusible signal factors (DFS) as future therapeutics. The above examples provide insight into the cutting-edge studies undertaken by GMU scientists to develop novel methodologies and platform technologies important to drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

Unraveling complex causal processes that affect sustainability requires more integration between empirical and modeling approaches

Scientists seek to understand the causal processes that generate sustainability problems and determine effective solutions. Yet, causal inquiry in nature–society systems is hampered by conceptual and methodological challenges that arise from nature–society interdependencies and the complex dynamics they create. Here, we demonstrate how sustainability scientists can address these challenges and make more robust causal claims through better integration between empirical analyses and process- or agent-based modeling. To illustrate how these different epistemological traditions can be integrated, we present four studies of air pollution regulation, natural resource management, and the spread of COVID-19. The studies show how integration can improve empirical estimates of causal effects, inform future research designs and data collection, enhance understanding of the complex dynamics that underlie observed temporal patterns, and elucidate causal mechanisms and the contexts in which they operate. These advances in causal understanding can help sustainability scientists develop better theories of phenomena where social and ecological processes are dynamically intertwined and prior causal knowledge and data are limited. The improved causal understanding also enhances governance by helping scientists and practitioners choose among potential interventions, decide when and how the timing of an intervention matters, and anticipate unexpected outcomes. Methodological integration, however, requires skills and efforts of all involved to learn how members of the respective other tradition think and analyze nature–society systems.

42 ENGINEERING↗

Optimal Heliostat Assignment Strategy for Multiple-Receiver Systems

Next-generation concentrating solar power concepts currently being developed under the U.S. Department of Energy's Gen3 program include several novel heliostat field and receiver arrangements. Among these concepts are solar fields utilizing multiple receivers or several distinct receiver flux control zones, and each of which has particular power or peak flux constraints. In this paper, we present a linear program that identifies optimal heliostat-to-receiver assignment strategies for multi-receiver or multi-zone solar fields based on detailed calculations made by an optics modeling package. Two distinct problems are addressed: namely, (1) a design problem that chooses the optimal set of heliostats to include in a final layout given receiver constraints and performance, and (2) an operations problem that assigns existing heliostats to a receiver, maximizing field power output while maintaining receiver power constraints. The optimization methodology is integrated into the concentrating solar power tower modeling package SolarPILOTTM, and results are presented showing that heliostat assignment strategies can impact both initial heliostat field layout characteristics and the cost of thermal energy produced by the field. The methodology succeeds in maintaining power requirements but at the expense of producing different flux profiles on each receiver surface. Consequently, the paper also discusses additional measures that are applied to ensure that desirable flux distribution properties are maintained, reducing local flux mean-absolute-deviation values from a baseline of 17% or greater to less than 5% compared to a desired reference flux profile.

41 EE - Solar Energy Technologies Office (EE-4S)↗

Continuum radiated power density $(P^{\textrm{C}}_{\textrm{rad}})$ and effective charge $(Z_{\textrm{eff}})$ estimates from multi-energy photon-counting measurements

Multi-energy soft x-ray pinhole cameras have been designed, built, calibrated, and operated at Madison symmetric Torus, Alcator C-Mod, and more recently at Tokamak a configuration variable and Tungsten Environment in Steady-State Tokamak (WEST), to measure plasma emission across multiple energy ranges. Here we describe a new methodology to estimate the local continuum radiated power density and the plasma effective charge (Z eff ) directly from photon-counting measurements of the line-free continuum emission (Bremsstrahlung and Radiative Recombination) in several energy bands between 11 and 18 keV. This capability is particularly valuable for confinement systems using metal plasma-facing components, where x-ray losses from interactions with the sputtered wall can represent a significant fraction of the total radiated power (P rad ). The approach leverages a well-characterized detector responsivity, modeled by a complementary error function, and interprets the differential multi-energy measurements between adjacent energy levels through the probability density function of a Gaussian distribution. The implementation of this diagnostic technique is currently under development on the WEST tokamak, aiming at the goal of providing real-time P rad and Z eff measurements during long-pulse operation (up to 1000 s) in the 2026 campaign.

WEST↗

Design of Resilient Electric Distribution Systems for Remote Communities: Surgical Load Management using Smart Meters

This paper describes a systematic process of designing resilient electric distribution systems and microgrids using smart meters for surgical load management (SLM) as part of Advanced Metering Infrastructure (AMI). The work focuses on selection approach, integration, and interoperability aspects for AMI in microgrids. SLM is proposed as a granular control methodology for serving selective critical loads across different distribution feeders in the system during extreme events. The surgical load shedding as well as load pick-up provides a robust approach for maximizing critical load served in a resource-constrained electric distribution system or a microgrid. We present the case of a 20 MW islanded microgrid in Cordova, AK, USA, which is the demonstration site for field validation of resilience enhancement technologies for the DOE-funded Grid Modernization project RADIANCE. Cordova microgrid is an islanded distribution grid that provides an environment to prove the approach, and the techniques may also be applicable to other regional distribution systems.

microgrids↗

Structural Characterization of Deployed Thermoplastic and Thermoset Composite Tidal Turbine Blades

The National Renewable Energy Laboratory worked with Verdant Power to manufacture, and characterize, novel thermoplastic composite blades on their Gen5d 5 m diameter turbines at the Roosevelt Island Tidal Energy (RITE) site in the East River in N.Y. to demonstrate a low-cost manufacturing process for marine energy structures. Verdant had designed, manufactured, and deployed epoxy thermoset composite blades on three Gen5d turbines in October 2020. At a maintenance cycle in May 2021, a Gen5d turbine equipped with the NREL thermoplastic blades was deployed and retrieved in October 2021. Modal, static and fatigue structural characterization was performed on both blade types before and after the deployment. The static and modal test results showed that the thermoplastic blades were slightly stiffer than the epoxy blades both dry and after the deployment. The epoxy blades had about 8% increase in strain at the applied load after the deployment, whereas the thermoplastic blade strains were not changed significantly. Ultimately the thermoplastic blade failed during fatigue testing due to a crack which is thought to have initiated between the internal foam and the laminate at the location of the internal instrumentation. The Verdant Power Gen5d epoxy blades did not have this internal instrumentation and did not have this failure, performing adequately through the operational period. A lack of design information and fatigue data for both the thermoplastic laminates and the adhesive used means that further work is needed to fully understand his failure and the root cause analysis is ongoing. This paper provides details on the thermoplastic blade manufacturing, materials, test methodology and results.

17 WIND ENERGY↗

Developing socially and economically generative, resilient PV-energy systems for low- and moderate-income communities: Applications for Puerto Rico (Final Technical Report)

The nexus of energy and poverty is one of the most significant long-term challenges facing the energy and electricity sectors, with negative feedback loops between energy-related costs, risks, and insecurities, on the one hand, and diverse other social and economic insecurities, on the other, helping to undermine health, wellbeing, and resilience in low- and moderate-income communities around the US and the world (Biswas et al. 2022). The transition of energy systems to clean energy alternatives offers a potential opportunity to redesign future energy arrangements in ways that, instead, create beneficial social value for communities, reverses the energy-poverty nexus, and enhances community wellbeing (Biswas et al. 2020; Miller et al. 2018). The promise of clean energy transitions to address the energy-poverty nexus is especially significant when designed using principles of just energy transitions. To leverage just clean energy transitions to accomplish a reversal of the energy-poverty nexus requires improvement in three capabilities: (1) Understanding and mapping the community-scale dynamics and variability of the energy-poverty nexus, which vary considerably from community to community; (2) Evaluating the technical, social, and economic potential for different clean energy system designs to deliver social value to specific communities; and (3) Working collaboratively with communities to imagine and design clean energy systems solutions that meet their needs, fit their capabilities and contexts, and deliver multiple forms of community-desired social value/benefits that help reverse key facets of the energy-poverty nexus. This project developed a portfolio of novel tools, methodological approaches, and collaborative partnerships with four communities in Puerto Rico that further the objective of building these three capabilities.

14 SOLAR ENERGY↗

Design of Resilient Electric Distribution Systems for Remote Communities: Surgical Load Management Using Smart Meters: Preprint

This paper describes a systematic process of designing resilient electric distribution systems and microgrids using smart meters for surgical load management (SLM) as part of Advanced Metering Infrastructure (AMI). The work focuses on selection approach, integration, and interoperability aspects for AMI in microgrids. SLM is proposed as a granular control methodology for serving selective critical loads across different distribution feeders in the system during extreme events. The surgical load shedding as well as load pick-up provides a robust approach for maximizing critical load served in a resource-constrained electric distribution system or a microgrid. We present the case of a 20 MW islanded microgrid in Cordova, AK, USA, which is the demonstration site for field validation of resilience enhancement technologies for the DOE-funded Grid Modernization project RADIANCE. Cordova microgrid is an islanded distribution grid that provides an environment to prove the approach, and the techniques may also be applicable to other regional distribution systems.

microgrids↗

Hydrogen Sustainable Infrastructure Guidelines (H2SIG)

Infrastructure interacts with major global challenges inclusive of climate change, resilience, equity and social justice, environmental protection and biodiversity, public health, and economic recovery. This effort directly addresses how to apply sustainability assessments to the unique aspects of hydrogen (H2) infrastructure projects using the Envision® sustainability framework developed by the Institute for Sustainable Infrastructure (ISI). Envision is a flexible system of criteria and performance objectives to aid decision makers and help project teams identify sustainable approaches during planning, design and construction of infrastructure projects that will continue throughout the project’s operations, maintenance, and end-of-life phases. The goal of this work is to support those interested in conducting sustainability assessments for hydrogen infrastructure projects. Through this work the team has developed hydrogen-specific guidelines and best practices to assist project developers, investors and regulators in straightforward application of the Envision framework to systematically enhance clarity and increase objectivity across hydrogen projects. Subject matter experts from 20 different organizations (industry, non-profits, municipalities, emissions regulators, national labs) were engaged through 7 focus groups to discuss the H2-prioritized Envision requirements and elements to be included in the guidelines. In addition to these guidelines, the team conducted case studies as a way of demonstrating and informing the guidelines and providing examples or ideas for implementing sustainability in future projects. Through this study, the authors found that hydrogen infrastructure projects can incorporate many sustainable practices including reducing GHG emissions affecting climate change, reducing air pollutant emissions affecting the health of communities, synergistically handling waste byproducts, integrating into existing infrastructure, incorporating renewable energy, and addressing equity and environmental justice. Additionally, hydrogen infrastructure projects could benefit from 2-way communication engagement and collaborative planning with stakeholders, educational awareness of hydrogen technology, tracking and reporting on water use and water quality, and including monetized environmental and social benefits in life-cycle cost estimates. This document is the final technical report for the Hydrogen Sustainability Assessment Methods for Project Development project. While the main focus of the report is to provide guidelines for applying sustainability to hydrogen infrastructure, there are sections within the appendix providing completed project tasks and methodology.

08 HYDROGEN↗

Adaptive Laboratory Evolution for algal strain improvement: methodologies and applications

Originally developed in bacterial and fungal models, Adaptive Laboratory Evolution (ALE) experimentally selects for targeted phenotypes by allowing cells containing beneficial random genetic mutations to replicate more quickly in the presence of environmental stress. When applied to microalgae, this technique has been used to increase growth rate, stress tolerance, substrate utilization, and product yield. This review discusses ALE experimental design parameters, and summarizes key results and lessons learned from recent microalgal ALE studies. By documenting the current state of the field, we provide insight on the ways microalgal ALE might be used to develop commercially viable biotechnology strains.

09 BIOMASS FUELS↗

Interparticle Characterization of Mechanical Biomass Particle-Particle and Particle-Wall Interactions

The biomass materials industry faces significant challenges in managing material variability and its impact on storage and handling systems. Physical properties such as moisture content, particle size, and density fluctuate considerably, leading to operational issues like bridging and ratholing that disrupt material flow. These variations create a complex cascade effect throughout the process chain, affecting transportation, storage, and conversion processes. The economic consequences of this variability manifest in increased operational costs, maintenance requirements, and system downtime. Environmental factors further complicate the situation, as weather conditions and seasonal availability influence material properties and system performance. Engineers employ specialized equipment design, material characterization protocols, and pre-processing steps like size reduction and homogenization to address these challenges. A critical knowledge gap exists between continuous-level constitutive models and particle-scale behavior. This project developed a novel device to quantify interparticle mechanics between biomass particles, measuring friction and adhesion forces between particles and wall materials. The research focused on corn stover and southern pine forest residue, creating a comprehensive database of particle interactions. This breakthrough enables direct application in particle-based computational modeling, advancing the field's understanding of biomass handling characteristics and supporting the development of more reliable and efficient storage and handling systems. The project's outcomes contribute significantly to understanding biomass's mechanical and flow characteristics, particularly how variability at the particle level affects larger-scale handling operations. This knowledge is crucial for engineering feedstock supply systems that consistently meet quality and cost specifications for various conversion processes. The innovative experimental setup developed through this research represents a significant advancement in biomass characterization methodology. Providing precise measurements of particle-level interactions establishes a foundation for more accurate predictive modeling of bulk material behavior. This enhanced understanding of fundamental particle mechanics enables engineers to anticipate better and address handling challenges before they manifest in full-scale operations. This research opens new avenues for optimizing biomass handling systems through data-driven design approaches. The comprehensive database of particle interactions serves as a valuable resource for future research and development efforts, potentially leading to more efficient and cost-effective biomass processing solutions. This advancement in particle-level mechanics could revolutionize how biomass handling systems are designed and operated, contributing to more sustainable and reliable renewable energy production.

09 BIOMASS FUELS↗

Validation of (not-historical) large-event near-fault ground-motion simulations for use in civil engineering applications

Ground-motion simulations generated from physics-based wave propagation models are gaining increasing interest in the engineering community for their potential to inform the performance-based design and assessment of infrastructure residing in active seismic areas. A key prerequisite before the ground-motion simulations can be used with confidence for application in engineering domains is their comprehensive and rigorous investigation and validation. This article provides a four-step methodology and acceptance criteria to assess the reliability of simulated ground motions of not historical events, which includes (1) the selection of a population of real records consistent with the simulated scenarios, (2) the comparison of the distribution of Intensity Measures (IMs) from the simulated records, real records, and Ground-Motion Prediction Equations (GMPEs), (3) the comparison of the distribution of simple proxies for building response, and (4) the comparison of the distribution of Engineering Demand Parameters (EDPs) for a realistic model of a structure. Specific focus is laid on near-field ground motions (<10km) from large earthquakes (M w 7), for which the database of real records for potential use in engineering applications is severely limited. The methodology is demonstrated through comparison of (2490) near-field synthetic records with 5 Hz resolution generated from the Pitarka et al (2019) kinematic rupture model with a population of (38) pulse-like near-field real records from multiple events and, when applicable, with NGA-W2 GMPEs. Lastly, the proposed procedure provides an effective method for informing and advancing the science needed to generate realistic ground-motion simulations, and for building confidence in their use in engineering domains.

42 ENGINEERING↗

Advanced Modular Sub-Atmospheric Hybrid Heat Engine (Final Report)

The Phase 1 Final Technical Report describes the results of the work completed during the “Advanced Modular Sub-Atmospheric Hybrid Heat Engine” project. A key part of the Phase 1 work was the completion of a thermodynamic cycle analysis for the MHHE at the selected module size. The hybrid heat engine has been developed as a modular unit (sized in the range of 500kW – 60MW) that can be used with modular coal or biomass gasifiers, with distributed power generation systems, with large power plants comprised of multiple generating units, and with natural gas compression stations. The MHHE will provide cleaner, more efficient, and lower cost generation with better load following capabilities than existing competing technologies with a singular generating source such as solar farm, gas turbine, or combustion engine. The drivers of the MHHE technology are: benefits of modular power generation (reduced equipment cost, construction cost and implementation time, connection ready on delivery, flexible scalability, serviceability), fuel flexibility, lowest cost power generation and reduced emissions.A logical progression of work and a clear path forward toward meeting the FOA goals and objectives have been established. Namely, a preliminary market analysis and primary fuel identification was completed first and then the modularity of the system was defined. The benefits of the proposed hybrid and modular heat engine were described when applied to modular coal gasifiers, distributed power generators, and larger power plants. Based on the market analysis, modularity, and chosen primary fuel, a conceptual design and layout of the hybrid heat engine was developed, analyzed, and characterized. The technology gaps already identified have been reviewed and expanded upon, and a test plan to address these gaps through bench scale testing in Phase 2 has been developed. Cost estimate methodology and considerations in support of a potential Phase 2 project have been described.

08 HYDROGEN↗

Initial development of a high temperature life prediction method directly accounting for variability in material properties

This report presents the initial development of a life prediction method that accounts for the variability of the material properties in Grade 91 steel. We account for material variability by both fitting a variable 3-parameter Weibull distribution to the rupture data of the material and by quantifying the effect that the creep deformation properties have on stresses via a Monte Carlo approach. To ensure a reasonable time frame when applying Monte Carlo to the creep stress, we propose a methodology that models the material as an extremely viscous Stokes flow with a non-Newtonian viscosity, therefore solving the problem in a single step. We use the final probabilistic model for structural failure developed in this document to evaluate a Flat Head Vessel designed with the Section III, Division 5 ASME Boiler Pressure and Vessel Code rules with a 100,000h design life and assess its probability of premature failure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Characterization of U Isotope Ratios by Laser Ablation MC-ICP-MS for IAEA Safeguards

This project will develop methodology to identify and analyze U and Pu containing particles using laser ablation MC-ICP-MS. The proposed work will involve: i) setup and optimization of the LA-MC-ICP-MS system, ii) testing and validation by analysis of QC standards, iii) designing analytical protocols for single particles, and iv) developing a data processing system. Ultimately, this project will output a detailed operating procedure documenting the experimental techniques and data processing routines required to perform particle identification and analysis by laser ablation MC-ICP-MS, that can be shared with IAEANWAL laboratories.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Observations and Lessons Learned From Installing Residential Roofing-Integrated Photovoltaics

Building-sited solar photovoltaics (PV) could play a key role in decarbonizing the building sector either through racked and mounted PV or through Building-integrated PV (BIPV). BIPV is installed into the building envelope itself, with solar cells and/or modules forming the outer layer of a building structure, thus transforming a single-purpose structure into one that serves the dual purposes of the building envelope and electricity. BIPV can be applied to building roofs, facades, awnings, pergolas, windows, skylights, balustrades, and other external surfaces. Given BIPV products vary widely, the focus of this research is residential roofing integrated PV (RIPV), where solar is incorporated into or otherwise replaces the roofing material. Previous research suggests that residential RIPV could reduce customer acquisition, labor, supply chain, and equipment costs. These products have yet to realize these cost savings and deployment remains significantly less than conventional rooftop PV as a relative share of the addressable market in the US. One potential barrier to broader residential roofing integrated PV deployment may be higher costs relative to conventional rooftop PV, primarily because the design and installation of these products is still evolving. Here, we explore residential RIPV cost-reduction opportunities by analyzing installation processes. Our study documents residential RIPV installations at 2 reroofing sites and the equivalent of 9 new construction sites in California through a methodology known as time and motion study. We also conducted interviews with subject-matter experts to identify barriers and solutions to maximize these products' market penetration. Our time and motion study breaks the RIPV installation process into four steps: 1) staging, unloading, and roof preparation; 2) fire resistant underlayment(s) (synthetic material laid between roof shingles and roof deck); 3) flashings and PV installation; and 4) wiring and monitoring. We measure the time required for each step in terms of worker-hours, representing an hour of labor from a single worker. We further normalize process time by dividing worker-hours by kilowatt (kW) of system capacity. The most time-intensive step was flashings and PV installation, taking around 2.4 worker-hours per kW on average and accounting for around 60% of the process time for an average installation. The total installation process took on average about 6.4 and 3.5 worker-hours per kW at the reroofing sites and new construction sites, respectively. For comparison, a previous time and motion study documented a time of 6.9 worker-hours per kW for conventional rooftop PV. The shorter RIPV installation times are consistent with previous studies suggesting that RIPV could be installed faster than conventional rooftop PV. The time and motion results and feedback from interviewees provide insights into potential residential RIPV cost reduction opportunities. Several interviewees suggested that these products would be more efficient if PV installation was more fully integrated into the roofing/construction industries, which currently use separate supply chains and skillsets. Further integration could reduce supply chain delays and labor force redundancies. Future research could explore specific ways to integrate these industries to help realize the cost savings potential of RIPV.

14 SOLAR ENERGY↗

Development of Analytical Method for Measuring U and Pu Particles by Laser Ablation MC-ICP-MS for the NWAL

This project will develop methodology to identify and analyze U and Pu containing particles using laser ablation MC-ICP-MS. The proposed work will involve: i) setup and optimization of the LA-MC-ICP-MS system, ii) testing and validation by analysis of QC standards, iii) designing analytical protocols for single particles, and iv) developing a data processing system. Ultimately, this project will output a detailed operating procedure documenting the experimental techniques and data processing routines required to perform particle identification and analysis by laser ablation MC-ICP-MS.

07 ISOTOPE AND RADIATION SOURCES↗

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.

60 APPLIED LIFE SCIENCES↗