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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

High-efficiency single-junction p-i-n GaAs solar cell on roll-to-roll epi-ready flexible metal foils for low-cost photovoltaics

We demonstrate the fabrication and characterization of high-efficiency, single-junction p-i-n GaAs solar cells, on flexible metal foil with epi-ready buffer via roll-to-roll fabrication. Single-junction p-i-n GaAs solar cells were fabricated using metal–organic chemical vapor deposition (MOCVD). Furthermore, an efficiency greater than 13% was obtained at 1 sun, which is the highest reported efficiency on GaAs photovoltaics directly deposited on metal tapes. This exceeds our previously reported study showcasing 11.5% efficiency on single-junction p-n solar cell structure. Improved morphology of p-i-n structure compared with p-n is explained by atomic force microscopy (AFM), scanning electron microscopy (SEM), and helium ion microscopy (HIM) measurements. Time-of-flight secondary ion mass spectrometry (TOF-SIMS) analysis showed reduced Zn diffusion in p-i-n cell compared with the p-n cell. We attribute the improvement in efficiency of p-i-n cells on flexible metal tapes to the quality of the junction, surface morphology, controlled diffusion of species within the active layers, and increase in absorption due to the optimized intrinsic layer thickness.

14 SOLAR ENERGY↗
DOE OSTI2021

A techno-economic framework for comparing conventionally and additively manufactured parts for geothermal applications

Geothermal reservoir characterization, construction, and operations are technology-intensive activities that contribute significantly to the cost of delivering renewable electricity. The technologies involved, such as downhole tools and drilling equipment, are similar to those used in oil and gas exploration and production must often be adapted for use in the corrosive, high-temperature geothermal reservoir environment. Low production volume of geothermal tools presents a major challenge in meeting the industry's technology needs. Production of specialized tools for geothermal subsurface applications is often cost-prohibitive. Reduced inventory of subsurface well construction, characterization, and production tools causes geothermal reservoir development efficiency and sophistication to lag behind that of the oil and gas industry. Advances in additive manufacturing provide opportunities to advance geothermal technology while reducing lead time and costs associated with production of low-volume, complex parts. Additionally, this paper performs an initial techno-economic analysis comparing the cost of conventional production techniques and additive manufacturing for geothermal downhole applications. An analysis of representative downhole tools is used to create a framework for estimating fabrication costs of subtractive and additive techniques, including post-print machining required to meet final tolerances. The framework is used to explore several manufacturing scenarios and identify the dominant factors driving manufacturing time and cost. The current feasibility of additive manufacturing for geothermal downhole tool applications is assessed and issues for future development to better meet the needs of the geothermal industry are identified.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Advancements of On-Line Monitoring and Real-Time Characterization of Actual Low-Level Hanford Tank-Waste Samples

Waste and process stream characterization is essential to the safe, efficient, and cost-effective processing of Hanford Site materials. The demand for materials characterization including chemical, radionuclide, and physical attributes during waste management, transfer, and staging operations in the Hanford tank farms is expected to significantly increase as the U.S. Department of Energy River Protection Project progresses toward Hanford waste treatment and immobilization. Our past work has applied Raman on-line monitoring and chemometric modeling to the supernate of Hanford tank 241-AP-105 and has demonstrated the quantitative measurement of nine analytes within this waste. To demonstrate broader applicability, this method has now been expanded to waste from two additional Hanford tanks, AW-102 and AP-107, and the offgas condensate from lab-scale melter runs of actual-tank waste feeds. To enhance the performance of the Raman method, instruments with various excitation wavelengths were compared, since it is known that the Raman response is enhanced by shorter wavelength excitation. The laser excitations for the three systems tested were 404 nm, 532 nm, and 671 nm. In addition to laser wavelength selection, the laser power, measurement integration times, and signal averaging techniques were also investigated to determine their effect on detection limits for oxy-anion analytes within tank wastes. These advancements in Raman capability are compared with past standards and will be presented.

Bryan, Samuel A.↗
DOE OSTI2020

Predicting Geometric Influences in Metal Additive Manufacturing

Metal additive manufacturing (AM) has evolved from scientific curiosity to a potential paradigm shift in materials manufacturing owing to its ability to control microstructure and fabricate complex geometries. However, geometry, often ignored as a variable, can have a significant impact on microstructure evolution, thereby indirectly limiting the design flexibility offered by AM. Currently, the process of retaining microstructure with changing geometry relies on trial and error-based processing followed by microstructural evaluation that places significant demands on time and costs associated with experimentation and characterization. We demonstrate herein that lightweight thermal models can be effectively used to predict microstructural changes with geometry. These models can in turn be used as precursors to developing geometry independent scan strategies and achieve microstructure control in various alloy systems. Even if the thermal signatures predicted by the model are not accurate, scan strategies can be developed to mimic the thermal conditions during initial process development. We present a case study on one of the most studied AM alloys: Ti-6Al-4V.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Understanding intercalation chemistry for sustainable aqueous zinc–manganese dioxide batteries

Rechargeable aqueous Zn-MnO 2 technology combines one of the oldest battery chemistries with favourable sustainability characteristics, including safety, cost and environmental compatibility. However, the ambiguous charge storage mechanism presents a challenge to fulfil the great potential of this energy technology. In this work, we leverage on advanced electron microscopy, electrochemical analysis and theoretical calculations to look into the intercalation chemistry within the cathode material, or α-MnO 2 more specifically. We show that Zn 2+ insertion into the cathode is unlikely in the aqueous system; rather, the charge storage process is dominated by proton intercalation to form α-H x MnO 2 . We further reveal anisotropic lattice change as a result of entering protons proceeding from the surface into the bulk of α-MnO 2 , which accounts for the structural failure and capacity decay of the electrode upon cycling. Our work not only advances the fundamental understanding of rechargeable zinc batteries but also suggests the possibility to optimize proton intercalation kinetics for better-performing cell designs. A rechargeable aqueous Zn-MnO 2 battery features a combination of favourable sustainability characteristics from safety to cost. The authors deploy advanced characterizations and theoretical calculations to provide fresh insight into the charge storage mechanism, which not only closes an ongoing debate but suggests ways forward.

25 ENERGY STORAGE↗
DOE OSTI2020

Chapter 1: Introduction - Motivation of Polycrystalline Thin-Film Solar Cells

Thin-film solar cell development may be conceptualized as improving processes to lower costs and improve performance, with characterization as a guide along the way. Yet, pushing the boundaries of characterization is as much at the core of thin-film solar cell development as material synthesis. As PV continues to mature, there is an endless drive to improve performance to compete. Hence, PV technology is striving for increasing levels of perfection, requiring a corresponding sophistication in characterization and understanding. Consequently, the topic of this book -advanced characterization-is timely and critical to develop the potential of thin-film solar technology.

material synthesis↗
DOE OSTI2022

Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data

As the number of phasor measurement units (PMUs) deployed in a power system increases, and their data volume streamed to the control canter intensifies, operators are facing challenges related to the analysis of such data, which need to be observed and responded to as the measurements are displayed in the Control Room. Humans are generally unable to process such large amount of data efficiently and rapidly. There is an apparent need for automated ways to analyze the data, extract actionable information about occurrence of specific events, and characterize the events quickly and cost effectively. This paper discusses the use of machine learning (ML) to facilitate such tasks by providing automated, highly computationally efficient, and cost-effective ways of extracting actionable information from synchrophasor big data in real-time. We developed Big Data Smart (BDSmart) ML-based prototype tool for the Control Room use that automatically analyses data properties from synchrophasor system measurements taken across the three grid Interconnections in the USA (Western, Eastern and ERCOT). The data collected from several hundreds of PMUs located across the Interconnections over a period of two years have been made available for our extensive study. As a result, we were able to identify a number of big data properties that influence how ML methodology is applied to select, develop, train and test the data models that can eventually be used for the tool implementation. The resulting set of candidate algorithms spans unsupervised, supervised, semi-supervised and transfer-learning approaches. Many ML techniques, such as decision trees, multinomial logistic regression, feed-forward neural networks, K-nearest neighbor, multiclass support vector machine, and single and multi-channel convolutional neural networks, are implemented, and their performance is examined. We offer the results from testing the data models. The novelty of our study is in the approaches for bad data detection and mitigation, selection of a simplified feature for event detection, and data label improvements. As a result, we came up with a list of recommendations for the utilities on how to improve the PMU recording practices to cater to the future ML applications aimed at automating the analysis of synchrophasor data.

Synchrophasors, Machine Learning, System-wide Even↗
DOE OSTI2023

Surface Roughness Effects from Additive Manufacturing in High Efficiency Gas Turbine Combustion Systems

In the past, clean combustion systems were characterized by high system development costs due to growing complexity and escalating manufacturing costs from conventional manufacturing processes. As a result, high efficiency concepts are difficult to design and more difficult to be cost-effectively manufactured. In recent years, with the introduction of additive manufacturing (AM) technologies, the rapid prototyping and mass production processes of clean combustion systems are promising to be significantly simplified with significant reduction in terms of engine manufacturing cost and engine energy cost. Compared with conventional manufacturing built parts, the AM process enabled a simpler design to be adopted for the nozzle, reducing the number of required braze and weld joints from twenty-five to just five. The resulting nozzle was 25% lighter and five times more durable and contributed to a 15% reduction in fuel burn in comparison with the previous model produced. Applying these improvements to a fleet of 5,000 turbofan engines at 150 kN thrust would result in fuel cost savings of $ \$ $6B annually (at $ \$ $5/gallon Jet-A fuel price, 700 gallon/hour fuel consumption rate, and 2,300 operational hours per year), and reduce CO2 emissions by more than 25 million tons per year. These advances support core Department of Energy (DOE) missions in energy efficiency, improving productivity, and environmental sustainability. Maximizing the benefit of these new capabilities will require high prediction capability of high speed turbulent flow with wall modeled Large Eddy Simulation (LES). GE Aviation maintains that advanced simulation technology and supercomputing is required in order to provide the appropriate boundary conditions to realize the maximum potential of AM and Ceramic Matrix Composite to reduce cooling flow, a first order penalty on the Brayton cycle. The algorithm developed using the ANSYS/Fluent software would provide the foundation for entirely new avenues of research and development with potential multi-billion dollar impact to the US economy, and significant reduction in carbon based emissions across the aerospace and power generation industries. The formulation of this iWLES (integral wall model for LES) is generic and allows to capture the changes in flow dynamics that have a significant impact on the wall bounded flow characteristics, such as swirler effective area, bulk swirl number, local pressure distribution, exit velocity profile, and turbulence kinetic energy profile. A periodic channel flow with rough flat plate is simulated using LES (Wall-Adapting Local Eddy-viscosity) model to verify the implementation of iWLES in the Fluent User Defined Function (UDF). As the flow fields are highly sensitive due to surface roughness of the wall bounded flows in the combustion systems, there is significant potential to conduct further LES studies focusing on the turbulence boundary layer interaction. Accurately capturing near wall physics will elucidate the impact of rough surfaces on combustor flow and aero-thermal interactions.

42 ENGINEERING↗
DOE OSTI2023

Surface Roughness Effects from Additive Manufacturing in High Efficiency Gas Turbine Combustion Systems

In the past, clean combustion systems were characterized by high system development costs due to growing complexity and escalating manufacturing costs from conventional manufacturing processes. As a result, high efficiency concepts are difficult to design and more difficult to be cost-effectively manufactured. In recent years, with the introduction of additive manufacturing (AM) technologies, the rapid prototyping and mass production processes of clean combustion systems are promising to be significantly simplified with significant reduction in terms of engine manufacturing cost and engine energy cost. Compared with conventional manufacturing built parts, the AM process enabled a simpler design to be adopted for the nozzle, reducing the number of required braze and weld joints from twenty-five to just five. The resulting nozzle was 25% lighter and five times more durable and contributed to a 15% reduction in fuel burn in comparison with the previous model produced. Applying these improvements to a fleet of 5,000 turbofan engines at 150 kN thrust would result in fuel cost savings of $6B annually (at $5/gallon Jet-A fuel price, 700 gallon/hour fuel consumption rate, and 2,300 operational hours per year),and reduce CO2 emissions by more than 25 million tons per year. These advances support core Department of Energy (DOE)missions in energy efficiency, improving productivity, and environmental sustainability. Maximizing the benefit of these new capabilities will require high prediction capability of high speed turbulent flow with wall modeled Large Eddy Simulation (LES). GE Aviation maintains that advanced simulation technology and supercomputing is required in order to provide the appropriate boundary conditions to realize the maximum potential of AM and Ceramic Matrix Composite to reduce cooling flow, a first order penalty on the Brayton cycle. The algorithm developed using the ANSYS/Fluent software would provide the foundation for entirely new avenues of research and development with potential multi-billion dollar impact to the US economy, and significant reduction in carbon based emissions across the aerospace and power generation industries. The formulation of this iWLES (integral wall model for LES) is generic and allows to capture the changes in flow dynamics that have a significant impact on the wall bounded flow characteristics, such as swirler effective area, bulk swirl number, local pressure distribution, exit velocity profile, and turbulence kinetic energy profile. A periodic channel flow with rough flat plate is simulated using LES (Wall-Adapting Local Eddy-viscosity) model to verify the implementation of iWLES in the Fluent User Defined Function (UDF). As the flow fields are highly sensitive due to surface roughness of the wall bounded flows in the combustion systems, there is significant potential to conduct further LES studies focusing on the turbulence boundary layer interaction. Accurately capturing near wall physics will elucidate the impact of rough surfaces on combustor flow and aero-thermal interactions.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Assessment of Galvanic Corrosion in Molten Salts

Molten salts are an excellent choice for thermal energy storage materials in concentrated solar power (CSP) plants, yet some salts can cause corrosion of containment alloys that damage plants and increase maintenance costs. Corrosion rates have been characterized for many salt/alloy combinations. However, corrosion rates at junctions of dissimilar alloys in molten salts are relatively poorly characterized. At such junctions, which are common in CSP plants, it is possible that galvanic interactions or other mechanisms could increase corrosion above the industry standard of 20 µm/year. Preliminary reports measured corrosion at dissimilar alloy junctions with a variety of methods, but the results are not uniform. Additionally, some reports have shown that corrosion rates are higher for single alloys than for the same alloys when paired in a galvanic couple, suggesting complex corrosion mechanisms. Here we present a discussion on measuring galvanic corrosion in molten salts that accounts for differences in results between methods and explores the possibility of other mechanisms of corrosion in the system.

Massimi, Scott Edward (ORCID:0000000244008093)↗
DOE OSTI2020

Advances in Resin Management Using 3R-Scan - 20154

The most important factor underlying optimal waste management is developing a clear picture of the radioactivity content of the waste and its impact on waste disposal cost. For over 35 years since the publishing of 10CFR61, waste characterization has relied on sampling the final waste product after formation. In the days following 10CFR61, the cost of final disposal was marginal with only a small impact on the overall costs. Constraints were added by provisions of the Low Level Waste Policy Act of 1985 leading to increasingly limited access to those disposal sites that remained available. In addition, Nuclear Regulatory Commission (NRC) pressure promoting waste volume reduction led to disposal costs inevitably rising. Despite this, characterization practices in monitoring of waste generation for activity content still center on the same dated processes. This results in a disposal classification on the basis of endpoint sampling without consideration of the homogeneity of the waste mixture. It can also disregard consideration of the representativeness of the single or small sample base. As a minimum effort, a formalized sampling program of a fixed grouping of waste streams can be implemented that could account for more than 95% of all of the activity carried in solid waste products. The sample results for each radionuclide could then be trended as time passes to develop reasonable scaling factors for difficult to measure radionuclides. This process, identified in NRC guidance, has been rigorously followed by a relatively small number of facilities. The trended scaling factors serve to improve accuracy by identifying anomalous results that could otherwise go undetected. Direct monitoring of the accumulation of activity in process streams generating solid radwaste, including demineralizers and filter streams, is a more precise approach. Nearly all of these streams are monitored by plant chemistry on a regular schedule to maintain water quality. This paper discusses viable options for developing the basis for characterization through process monitoring of the accumulation of activity at the point of generation. Special focus is on resin bed tracking and how process knowledge of these streams can be brought together to form a consistent and precise solid waste radioactivity inventory. Some of the specific points covered in this paper include the merger of the fission product release computer program, 3R-STAT, with the radwaste sample analysis computer program, SCAN4 to create 3R-SCAN, the importance of individual waste stream influences on the overall source term, and the use of historic sample data to develop scaling factors using an automated process. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Full-Length ASFV B646L Gene Sequencing by Nanopore Offers a Simple and Rapid Approach for Identifying ASFV Genotypes

African swine fever (ASF) is an acute, highly hemorrhagic viral disease in domestic pigs and wild boars. The disease is caused by African swine fever virus, a double stranded DNA virus of the Asfarviridae family. ASF can be classified into 25 different genotypes, based on a 478 bp fragment corresponding to the C-terminal sequence of the B646L gene, which is highly conserved among strains and encodes the major capsid protein p72. The C-terminal end of p72 has been used as a PCR target for quick diagnosis of ASF, and its characterization remains the first approach for epidemiological tracking and identification of the origin of ASF in outbreak investigations. Recently, a new classification of ASF, based on the complete sequence of p72, reduced the 25 genotypes into only six genotypes; therefore, it is necessary to have the capability to sequence the full-length B646L gene (p72) in a rapid manner for quick genotype characterization. Here, we evaluate the use of an amplicon approach targeting the whole B646L gene, coupled with nanopore sequencing in a multiplex format using Flongle flow cells, as an easy, low cost, and rapid method for the characterization and genotyping of ASF in real-time.

Virology↗
DOE OSTI2021

Impacts of turbine and plant upsizing on the levelized cost of energy for offshore wind

Turbine and plant upsizing are major trends in offshore wind deployment, although the quantitative impact on project costs has not been well-characterized. The uncertain value of continued wind turbine and project growth limits the ability of the supply chain to prepare for future technology trends, leading to challenges in the realization of larger projects. This analysis explores the levelized cost of energy impacts of turbine ratings between 6 and 20 MW and plant capacities between 250 and 2,500 MW for fixed-bottom offshore wind using techno-economic cost models for foundation, electrical, installation, and operation and maintenance costs, along with annual energy production. We consider a nominal set of technology assumptions for all scenarios to isolate economies of size and scale without additional benefits from decreasing turbine capital costs, quantity discounts for larger projects, or optimized technology solutions. These results indicate that using a 20-MW wind turbine in a 2,500-MW power plant array can reduce the levelized cost of energy by over 23% relative to the global average turbine and plant size installed in 2019; primarily because of reductions in the balance-of-system and operation and maintenance costs. We also identify improved installation vessels, optimized export systems, and novel operation and maintenance strategies as additional cost reduction opportunities. These results suggest that upsizing represents a significant cost reduction opportunity for offshore wind energy and will continue to be a main factor in shaping the future of the sector.

17 WIND ENERGY↗
DOE OSTI2020

Mesoscopic analyses of the impact of morphology and operating conditions on the transport resistances in a proton-exchange-membrane fuel-cell catalyst layer

Exploring the origins of local transport resistance and characterizing the oxygen transport resistances in the catalyst layer (RCL) are critical for cost reduction. In this paper, a comprehensive mesoscopic model for simulating coupled transport processes of oxygen and water vapor for different structural parameters under different operating conditions in reconstructed microstructures is proposed. The local transport resistance is calculated after achieving the limiting current density and the effective diffusivity of oxygen. The results demonstrate that RCL increases greatly with decreasing platinum loading (LPt) and the transport resistances in other components of the cell dominate for high-loadings. Both the reduced oxygen permeation coefficient in the ionomer thin-film and the adsorption resistance account for the origins of local transport resistance. The local transport resistance increases with the bare carbon ratio for a constant LPt and Pt/C ratio due to the decreased effective ionomer surface, and increases with the I/C ratio due to the increased ionomer thickness and decreased Knudsen diffusivity. Due to the presence of liquid water, a slight decrease followed by an increase of the local transport resistance versus relative humidity is obtained. The contribution of ionomer thin-films to RCL is more sensitive to liquid saturation compared with that of pores. This journal is

Mu, YT↗
DOE OSTI2020

Analysis of a Fluidized-Bed Particle/Supercritical-CO2 Heat Exchanger in a Concentrating Solar Power System

Concentrating solar power (CSP) development has focused on increasing the energy conversion efficiency and lowering the capital cost. To improve performance, CSP research is moving to high-temperature and high-efficiency designs. One technology approach is to use inexpensive, high-temperature heat transfer fluids and storage, integrated with a high-efficiency power cycle such as the supercritical carbon dioxide (sCO 2 ) Brayton power cycle. The sCO 2 Brayton power cycle has strong potential to achieve performance targets of 50% thermal-to-electric efficiency and dry cooling at an ambient temperature of up to 40 °C and to reduce the cost of power generation. Solid particles have been proposed as a possible high-temperature heat transfer or storage medium that is inexpensive and stable at high temperatures above 1000 °C. The particle/sCO 2 heat exchanger (HX) provides a connection between the particles and sCO 2 fluid in emerging sCO 2 power cycles. This article presents heat transfer modeling to analyze the particle/sCO 2 HX design and assess design tradeoffs including the HX cost. The heat transfer process was modeled based on a particle/sCO 2 counterflow configuration, and empirical heat transfer correlations for the fluidized bed and sCO 2 were used to calculate heat transfer area and estimate the HX cost. A computational fluid dynamics simulation was applied to characterize particle distribution and fluidization. This article shows a path to achieve the cost and performance objectives for a particle/sCO 2 HX design by using fluidized-bed technology.

14 SOLAR ENERGY↗
DOE OSTI2021

Escaping the fate of Sisyphus: assessing resistome hybridization baits for antimicrobial resistance gene capture

Finding, characterizing and monitoring reservoirs for antimicrobial resistance (AMR) is vital to protecting public health. Hybridization capture baits are an accurate, sensitive and cost-effective technique used to enrich and characterize DNA sequences of interest, including antimicrobial resistance genes (ARGs), in complex environmental samples. We demonstrate the continued utility of a set of 19 933 hybridization capture baits designed from the Comprehensive Antibiotic Resistance Database (CARD)v1.1.2 and Pathogenicity Island Database (PAIDB)v2.0, targeting 3565 unique nucleotide sequences that confer resistance. We demonstrate the efficiency of our bait set on a custom-made resistance mock community and complex environmental samples to increase the proportion of on-target reads as much as >200-fold. However, keeping pace with newly discovered ARGs poses a challenge when studying AMR, because novel ARGs are continually being identified and would not be included in bait sets designed prior to discovery. Here we provide imperative information on how our bait set performs against CARDv3.3.1, as well as a generalizable approach for deciding when and how to update hybridization capture bait sets. This research encapsulates the full life cycle of baits for hybridization capture of the resistome from design and validation (both in silico and in vitro) to utilization and forecasting updates and retirement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Conotoxin Prediction: New Features to Increase Prediction Accuracy

Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series of diverse, complex, and labor-intensive biological, toxicological, and analytical techniques for effective characterization. While recent attempts, using machine learning based solely on primary amino acid sequences to predict biological toxins (e.g., conotoxins and animal venoms), have improved toxin identification, these methods are limited due to peptide conformational flexibility and the high frequency of cysteines present in toxin sequences. This results in an enumerable set of disulfide-bridged foldamers with different conformations of the same primary amino acid sequence that affect function and toxicity levels. Consequently, a given peptide may be toxic when its cysteine residues form a particular disulfide-bond pattern, while alternative bonding patterns (isoforms) or its reduced form (free cysteines with no disulfide bridges) may have little or no toxicological effects. Similarly, the same disulfide-bond pattern may be possible for other peptide sequences and result in different conformations that all exhibit varying toxicities to the same receptor or to different receptors. We present here new features, when combined with primary sequence features to train machine learning algorithms to predict conotoxins, that significantly increase prediction accuracy.

collisional cross section↗
DOE OSTI2025

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS↗