53 Search Results

Dedicated energy crops are promising feedstocks to make biofuels including jet fuels. This study applies life cycle analysis (LCA) to estimate direct well-to-wake (WTW) greenhouse gas (GHG) emissions (g CO2e/MJ) for jet fuel derived from five energy crops-biomass sorghum, miscanthus, switchgrass, poplar, and willow-via Fischer-Tropsch-to-Jet (FTJ) and Ethanol-to-Jet (ETJ) pathways. The WTW boundary includes direct emissions from biomass production, fuel production, and fuel combustion. The R&D GREET model is expanded to conduct the LCA, using national average biomass yields and farming inputs from the 2023 Billion-Ton Study. In addition, this study estimates emissions from market-mediated effects, including induced land-use change,more » induced other crop (non-feedstock) production changes, and induced livestock production changes using global economic and emissions factor models. On a per-dry U.S. ton basis, cultivation and harvest emissions are lowest for willow (51,565 g CO2e) and highest for biomass sorghum (104,488 g CO2e). Per-acre results show similarly high emissions for sorghum and lowest values for poplar and willow. Direct WTW emissions are substantially lower for FTJ (biomass sorghum: 5.5; miscanthus: 10.3; switchgrass: 11.7; poplar: 11.9; and willow: 8.7 g CO2e/MJ) than ETJ (33.2; 33.8; 34.8; 36.2; and 31.7 g CO2e/MJ, respectively). When market-mediated emissions are included, miscanthus exhibits the lowest total emissions across energy crop pathways. Although results are sensitive to modeling assumptions, they indicate that high-yielding perennial and woody crops, particularly when planted on marginal land, could significantly reduce WTW emissions for bio-jet fuels by combining low direct emissions with soil carbon gains and favorable market-mediated effects.« less

This study quantifies and compares the life cycle greenhouse gas (GHG) emissions of renewable diesel (RD), sustainable aviation fuel (SAF), and biodiesel (BD) produced from two U.S. canola production systems: 1) emerging intermediate winter canola, typically grown in double- or relay-cropping systems between the growing seasons of main crops, and 2) main canola, mostly spring canola but also including winter canola, which are grown as primary crops occupying the field for a full growing season. Using the Research and Development version of the Greenhouse gases, Regulated Emissions, and Energy use in Technologies (R&D GREET) model and the most up-to-date lifemore » cycle inventory data─field trial data for intermediate winter canola (>37,000 acres) and recent national survey data for spring canola─this life cycle analysis (LCA) estimates the direct emissions from canola cultivation and harvest, the conversion of canola into fuels, fuel transportation, and combustion. In addition, we account for market-mediated emissions associated with a scenario of 0.5 billion gallons per year of spring canola-based biofuels, including induced land use change (ILUC), induced other crop (nonfeedstock) production changes, and induced livestock production changes. For intermediate winter canola, these market-mediated effects were not modeled, as ILUC is expected to be negligible due to its integration into existing rotations, and data are currently insufficient to reliably quantify other market-mediated changes. The estimated life cycle direct emissions of RD/SAF derived from intermediate winter canola and main spring canola are about 32 and 33 g of CO2-equivalent per megajoule of fuel (g CO2e/MJ), respectively. Corresponding emissions for BD from intermediate winter canola and main spring canola are about 30 and 31 g of CO2e/MJ, respectively. Farming is the dominant emissions source for both canola systems, with intermediate winter canola and main spring canola emitting about 19 and 20 g of CO2e/MJ, respectively. ILUC and other induced changes increase emissions of main spring canola-derived RD/SAF and BD by about 18 and 17 g of CO2e/MJ, respectively. These results indicate that the GHG emissions of biofuels produced from the two canola systems may differ substantially due to the different land use dynamics of the systems.« less

Elevated turbidity levels pose significant public health risks by facilitating the transport of harmful pollutants, including metals, organic compounds, and pathogenic microorganisms into the surface water. These conditions create serious challenges for public recreational water use and drinking water treatment, leading to economic losses and health risks. This study utilizes water monitoring data in Pine Island Bayou, Texas, and develops a Sequence-to-Sequence (S2S) model to predict turbidity using Attention-based Gated Recurrent Units with Encoder-Decoder (AT-GRU-ED) and Long Short-Term Memory (LSTM), coupled with Variational Mode Decomposition (VMD). Compared to the model without VMD, the model demonstrates satisfactory 72-hour turbidity prediction performance,more » achieving MAEs of 2.60 and 3.29 NTU (reductions of 53% and 58%), RMSEs of 21.08 and 31.49 NTU (reductions of 82% and 80%), and R² values of 0.96 and 0.84 on the validation and test sets, respectively. Feature importance analysis reveals that water temperature is the dominant factor influencing seasonal turbidity patterns, while real-time hourly rainfall significantly contributes to short-term variability. Turbidity typically peaks within 48 hours after rainfall events due to lagged effects from surface runoff and upstream flow. Findings suggest suspending recreational water use and water supply pumping for three days after heavy rainfall can benefit public health and improve water treatment processes. Discharges above 100 m3/s are found to accelerate sediment dilution and transport, reducing turbidity levels more quickly after the peak. In conclusion, the proposed model demonstrates reliable 72-hour turbidity prediction, supporting decision-making for water treatment plant operations and providing early warning for public recreational water use.« less

Chorismate is a branch-point metabolite in the biosynthesis of aromatic amino acids, vitamins, antibiotics and various other aromatic products in bacteria, fungi and plants. Although 13 chorismate-utilizing enzymes have been identified in bacteria, only 6 have been described in plants, where an estimated 30% of all photosynthetically fixed carbon passes through chorismate. Here, in this study, we describe a biosynthetic gene cluster (BGC) consisting of five core genes, including two reductases, two methyltransferases and one glucosyltransferase. Genetic and biochemical evidence shows that these five enzymes collectively give rise to three biosynthetic pathways, each originating from chorismate: two parallel pathways producemore » a class of non-aromatic, isomeric compounds abundant in the roots of Arabidopsis thaliana, whereas the third pathway produces methylated and glucosylated chorismate derivatives that subsequently react non-enzymatically with glutathione. Genome analysis revealed that variants of this BGC are present in some but not all species in the Brassicaceae family. Taken together, our study uncovered a BGC, containing three chorismate-utilizing enzymes, that controls three distinct post-chorismate pathways in A. thaliana. This work not only advances our understanding of carbon flow in this model plant but also highlights that the biochemical complexity encoded by plant BGCs is greater than previously appreciated.« less

Simultaneous flooding&heat and droughts&heat events can potentially destabilize hydro-meteorological conditions to deteriorate the water quality of Neches River. Machine learning (ML) models utilizing wireless sensor measurements have been applied to predict water quality and optimize various water management strategies. This study aims to develop ML models to predict dissolved oxygen (DO) prediction under various hydro-meteorological conditions and enhance water management decision-making. Wireless sensor measurements of DO, water temperature, sample depth, conductivity, turbidity, and pH, along with discharge from the United States Geological Survey stations, are collected for model inputs at the Pine Island Bayou C749 station (PIB-C749) and Neches Rivermore » Saltwater Barrier (SWB). Multilayer perceptron neural networks, recurrent neural networks, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) with and without attention mechanism (AT) are tested to determine the best model, which is applied the rolling forecast method to predict 14-day DO. Traditional and recurrent transfer learning (TL and RTL) methods are adopted to overcome insufficient data at the SWB. The input feature importance analysis using the integrated gradients (IG) algorithm is applied to determine dominant inputs. The results show LSTM-based models are capable handling long sequential data. AT-BiLSTM and RTL-LSTM demonstrate the best performance at the PIB-C749 (RMSE=0.054) and the SWB (RMSE=0.028), respectively. TL and RTL methods significantly improve model performance at the SWB. DO, temperature, and pH show higher importance, consistent with hydrodynamics and water chemistry. Both best models are applied to predict 14-day DO and demonstrate reasonable performance for decision-making. Hydro-meteorological conditions of 2017 flood and 2012 drought events are simulated and reveal that possible hypoxia occurs after flooding due to increasing temperature and turbidity, and DO concentration decreases significantly under heat and drought conditions. In conclusion, LSTM-based models utilizing wireless sensor data can be a timely and effective approach to make appropriate decisions on water resource management.« less

Epitaxial MnTe films have recently seen a surge in research into their altermagnetic semiconducting properties. However, those properties may be extremely sensitive to structural and chemical modifications. We report a detailed investigation of the synthesis of the altermagnet α-MnTe on GaAs(111), which reveals the bulk defect structure of this material, the mechanism by which it releases strain from the underlying substrate, and the impact of oxidation on its surface. X-ray diffraction measurements show that α-MnTe layers with thicknesses spanning 45 to 640 nm acquire lattice parameters different from bulk, mostly due to thermal strain caused by the substrate rather thanmore » strain from the lattice mismatch. Through high-resolution transmission electron microscopy (TEM) measurement, we then unveil a misfit dislocation array at the interface, revealing the mechanism by which lattice strain is relaxed. TEM also reveals a stacking fault in the bulk, occurring along a glide plane parallel to the interface. The combination of TEM with polarized neutron reflectometry measurements finally reveals the impact of oxidation on the chemistry of the surface of uncapped MnTe. Furthermore, or findings highlight the subtle role of epitaxy in altering the structure of α-MnTe, providing potential opportunities to tune the altermagnetic properties of this material.« less

Hydrogen derived from bio-based sources, or biohydrogen (bioH₂), has the potential to reduce GHG emissions from industrial and transportation sectors, owing to the low carbon footprint and myriad applications like refinery operation, ammonia production, steel production, fuel cell, etc. To evaluate the commercialization potential of bioH₂ production, we modeled bioH₂ production and conducted techno-economic analysis (TEA) and life cycle analysis (LCA) of two facilities producing 50 metric tonnes of bioH₂ per day from cheese whey (CW) and solid food waste (SFW) through the integration of dark fermentation (DF) and microbial electrolysis cell (MEC) technologies. LCA results showed that CW andmore » SFW can produce carbon-negative bioH₂, with emissions of −8.6 and −8.0 kg GHG kg⁻¹ bioH₂ with carbon sequestration and renewable electricity resources, respectively, making bioH₂ potentially eligible for a tax credit of $$\$$3$$ kg⁻¹ H₂ based on provision 45 V of the U.S. Inflation Reduction Act (IRA). In this study, bioH₂ production treats waste streams to generate fresh water, thus, potentially can receive waste water treatment fee that varies with regions. The MEC capital cost dominates the bioH₂ cost, which is mainly determined by current density. With a current density of 20 A m⁻², the production cost for CW input varied between $$\$$17$ and $$\$$24$ kg⁻¹ bioH₂, while that for SFW input ranged from $$\$$29$ to $$\$$30$ kg⁻¹ bioH₂ under different operating conditions, considering the 45 V tax credit, waste water treatment fee and production revenue. If the current density increases to 100 A m⁻², the bioH₂ cost decreases to a range of $$\$$4.0$$–$$\$$6.9$ for CW and $$\$$5$$–$$\$$6$$ for SFW scenarios. This study also shows that low-cost bioH₂ can be produced using CW waste stream as feedstock.« less

Copper (Cu) interconnects are an increasingly important bottleneck in integrated circuits due to energy consumption and latency caused by the notable increase in Cu resistivity as dimensions decrease, primarily due to electron scattering at surfaces. Herein, the potential of a directional conductor, PtCoO ₂ , which has a low bulk resistivity and a distinctive anisotropic structure that mitigates electron surface scattering is showcased. Thin films of PtCoO ₂ of various thicknesses are synthesized by molecular beam epitaxy (MBE) coupled with a postdeposition annealing process and the superior quality of PtCoO ₂ films is demonstrated by multiple characterization techniques. The thickness‐dependentmore » resistivity curve illustrates that PtCoO ₂ significantly outperforms effective Cu (Cu with TaN barriers) and Ru in resistivity below 20.0 nm with a more than 6x reduction compared to effective Cu below 6.0 nm, having a value of only 6.32 μΩ cm at 3.3 nm. It is determined that grain boundary scattering can still be improved for even lower resistivities in this material system through a combination of experiments and theoretical simulations. PtCoO ₂ is therefore a highly promising alternative material for future interconnect technologies promising lower resistivities, better stability, and significant improvements in energy efficiency and latency for advanced integrated circuits.« less

Surface waterbodies are heavily exposed to pollutants caused by natural disasters and human activities. Empowering sensor technologies in water quality monitoring, sufficient measurements have become available to develop machine learning (ML) models. Numerous ML models have quickly been adopted to predict water quality indicators in various surface waterbodies. This paper reviews 78 recent articles from 2022 to October 2024, categorizing water quality models utilizing ML into three groups: Point-to-Point (P2P), which estimates the current target value based on other measurements at the same time point; Sequence-to-Point (S2P), which utilizes previous time series data to predict the target value at onemore » time point ahead; and Sequence-to-Sequence (S2S), which uses previous time series data to forecast sequential target values in the future. The ML models used in each group are classified and compared according to water quality indicators, data availability, and model performance. Widely used strategies for improving performance, including feature engineering, hyperparameter tuning, and transfer learning, are recognized and described to enhance model effectiveness. The interpretability limitations of ML applications are discussed. This review provides a perspective on emerging ML for surface water quality models.« less

Ensuring an adequate supply of feedstock is a key challenge to achieving a significant production volume of sustainable aviation fuels (SAFs). One promising feedstock for SAFs is intermediate winter oilseeds added to existing crop production as alternatives to the winter fallow period. In this work, three intermediate winter oilseed crops─camelina (Camelina sativa L. Crantz.), carinata (Brassica carinata), and pennycress (Thlaspi arvense L.)─are evaluated for their life cycle greenhouse gas (GHG) emissions when harvested and processed into hydroprocessed renewable jet (HRJ). We evaluate solvent extraction (SE) and mechanical pressing as alternative oil extraction methods and examine the impacts of various allocationmore » methods used in the oil extraction process. The GHG emissions reduction potential of camelina-, carinata-, and pennycress-derived HRJs are 50.4%, 65.2%, and 65.7%, respectively, compared to petroleum jet fuel, with SE as an oil extraction method and under mass-based allocation. Combined with the estimated production potential of camelina, carinata, and pennycress as intermediate winter oilseed, 2.5 billion gallons of HRJ can be produced annually, and the total GHG savings could amount to 19.3 million tons when these HRJs replace petroleum jet fuels. In addition, we collect literature information to assess the impacts of these intermediate winter oilseeds on indirect land use change emissions and soil organic carbon sequestration potentials.« less