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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Value Gradient Learning Approach in Power and Frequency Regulation of Grid-Connected Synchronverters

In this paper, a neural network adaptive critic design (ACD) method based on value gradient learning (VGL) is used to optimally control a grid-connected synchronverter. The main drawback of the traditional synchronverters is their infeasibility to face non-inductive grids. To be able to implement a synchronverter technique in any impedance angle, a neural network-based adaptive controller is used. The advantage of adaptive dynamic programing is its ability to adjust itself when it faces changes and uncertainties in the power system. The proposed VGL consists of two subnetworks: the critic network and the action network. The action network is trained during the operation, and the critic network can be pretrained offline or can be simultaneously trained with the action network. To compare the effectiveness of the traditional synchronverter to the VGL-based synchronverter, the simulation results are provided.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Addressing Load Imbalance in Bioinformatics and Biomedical Applications: Efficient Scheduling across Multiple GPUs

Computational bioinformatics and biomedical applications frequently contain heterogeneously sized units of work or tasks, for instance due to variability in the sizes of biological sequences and molecules. Variable-sized workloads lead to load imbalances in parallel implementations which detract from efficiency and performance. Many modern computing resources now have multiple graphics processing units(GPUs) per computer for acceleration. These multiple GPU resources need to be used efficiently through balancing of workloads across the GPUs. OpenMP is a portable directive-based parallel programming API used ubiquitously in bioscience applications to program CPUs; recently, the use of OpenMP directives for GPU acceleration has become possible. Here, motivated by experiences with imbalanced loads in GPU-accelerated bioinformatics applications, we address the load balancing problem using OpenMP task-to-GPU scheduling combined with OpenMP GPU offloading for multiply heterogeneous workloads – loads with both variable input sizes, and simultaneously, variable convergence rates for algorithms with a stochastic component – scheduled across multiple GPUs. We aim to develop strategies which are both easy to use and have lower overheads, and may be incorporated incrementally in existing programs which already make use of OpenMP for CPU-based threading in order to make use of multi-GPU computers. We test different combinations of input size variability and convergence rate variability, and characterize the effects of these different scenarios on the performance of scheduling strategies across multiple GPUs with OpenMP. We present several dynamic scheduling solutions for different parallel patterns, explore optimizations, and provide publicly available example computational kernels to make these strategies easy to use in programs. This work will enable application developers to efficiently and easily use multiple GPUs for imbalanced workloads found in bioinformatics and biomedical applications.

Thavappiragasam, Mathialakan↗
DOE OSTI2026

Measurements of three-flavor neutrino oscillations from a PISCES two-detector fit to the NOvA Experiment data

NOvA is a long-baseline neutrino oscillation experiment with two functionally identical detectors: a Near Detector (ND) at Fermilab, placed 1 km from the neutrino source, and a Far Detector (FD) located 810 km away from the ND in Minnesota. NOvA s primary physics goals are to measure the neutrino oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$ with high precision, determine the neutrino mass hierarchy, and constrain the value of $\delta_{CP}$, primarily via the study of muon neutrino to electron neutrino oscillation. Extracting values for oscillation parameters from fits to data usually relies on treating systematic uncertainties as nuisance parameters, a strategy that suffers from poor scalability as the number of uncertainties becomes larger. This work introduces PISCES (Parameter Inference with Systematic Covariance and Exact Statistics), a novel method that circumvents this scalability problem by encoding systematic uncertainties into a covariance matrix. PISCES utilizes a nested minimization in which optimal systematic pulls are first computed using the covariance matrix in an inner minimization step, then the oscillation parameters are profiled over in the outer minimization. PISCES also uses a Poisson Likelihood term, making it ideal for the inclusion of low-statistic samples in the fits. PISCES is a flexible framework that also supports complex fits, such as a joint Near and Far detector fit. In the standard NOvA analysis, oscillation parameters are extracted using an extrapolation technique in which the ND data indirectly constrain the FD prediction via a ratio method. PISCES, on the other hand, enables a simultaneous ND+FD fit, allowing the high-statistics ND data to directly constrain systematic uncertainties across all samples. This thesis presents the full PISCES joint ND+FD fit for the NOvA three-flavor analysis, details its implementation, and evaluates its performance through extensive robustness tests and fake data studies. It also provides a comparison between the PISCES joint ND+FD results and the standard NOvA extrapolation method using the full NOvA 10-year data set. The results demonstrate that PISCES can successfully fit NOvA data while incorporating the constraints from the ND detectors consistently, using physically motivated systematic uncertainties to account for data/MC discrepancies.

Rajaoalisoa, Miriama [Cincinnati U.]↗
DOE OSTI2020

Meiotic susceptibility for induction of sperm with chromosomal aberrations in patients receiving combination chemotherapy for Hodgkin lymphoma

Improvements in survival rates with gonad-sparing protocols for childhood and adolescence cancer have increased the optimism of survivors to become parents after treatment. Findings in rodents indicate that chromosomal aberrations can be induced in male germ cells by genotoxic exposures and transmitted to offspring and future generations with effects on development, fertility and health. Thus, there is a need for effective technologies to identify human sperm carrying chromosomal aberrations to assess the germ-line risks, especially for cancer survivors who have received genotoxic therapies. The time-dependent changes in the burden of sperm carrying structural chromosomal aberrations were assessed for the first time in a cancer setting, using the AM8 sperm FISH protocol which simultaneously detects abnormalities in chromosomal structure and number in sperm. Nine Hodgkin lymphoma (HL) patients provided 20 semen samples before, during, and after NOVP therapy (Novantrone, Oncovin, Velban and Prednisone) and radiation therapy that produced scattered gonadal doses from <0.05 to 0.6 Gy. Late meiosis was found to be the most sensitive to NOVP treatment for the production of sperm with chromosomal abnormalities, both in structure and number. Earlier stages of spermatogenesis were less sensitive and there was no evidence that therapy-exposed stem cells resulted in increased frequencies of sperm with abnormalities in chromosomal structure or number. This indicates that NOVP therapy may increase the risks for paternal transmission of chromosomal structural aberrations for sperm produced 32 to 45 days after a treatment with these drugs and implies that there are no excess risks for pregnancies conceived more than 6 months after this therapy. This clinical evaluation of the AM8 sperm FISH protocol indicates that it is a promising tool for assessing an individual’s burden of sperm carrying chromosomal structural aberrations as well as aneuploidies after cancer therapy, with broad applications in other clinical and environmental situations that may pose aneugenic or clastogenic risks to human spermatogenesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Interspace and Vacancy Modulation: Promoting the Zinc Storage of an Alcohol-Based Organic–Inorganic Cathode in a Water–Organic Electrolyte

Expanding interspace and introducing vacancies are desired to promote the mobility of Zn ions and unlock the inactive sites of layered cathodes. However, this two-point modulation has not yet been achieved simultaneously in vanadium phosphate. Here, in this work, a strategy is proposed for fabricating an alcohol-based organic–inorganic hybrid material, VO 1–x PO 4 ·0.56C 6 H 14 O 4 , to realize the conjoint modulation of the d-interspace and oxygen vacancies. Peculiar triglycol molecules with an inclined orientation in the interlayer also boost the improvement in the conversion rate of V 5+ to V 4+ and the intensity of the P—O bond. Their synergism can ensure steerable adjustment for intercalation kinetics and electron transport, as well as realize high chemical reactivity and redox-center optimization, leading to at least 200% increase in capacity. Using a water–organic electrolyte, the designed Zn-ion batteries with an ultrahigh-rate profile deliver a long-term durability (fivefold greater than pristine material) and an excellent energy density of ≈142 Wh kg –1 (including masses of cathode and anode), thereby substantially outstripping most of the recently reported state-of-the-art zinc-ion batteries. This work proves the feasibility to realize the two-point modulation by using organic intercalants for exploiting high-performance new 2D materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Simultaneous Improvements in Efficiency and Stability of Organic Solar Cells via a Symmetric‐Asymmetric Dual‐Acceptor Strategy

Abstract Simultaneously achieving improvements in power conversion efficiency (PCE) and stability is the main task of the current development stage of organic solar cells (OSCs). This work reports a symmetry–asymmetry dual‐acceptor (SADA) strategy to construct ternary devices, which is found to be feasible for increasing both the PCE and the operational lifetime of OSCs. In this contribution, the symmetric acceptor L8‐BO and the asymmetric acceptor BTP‐S9 are blended in equal proportions with polymer donor PM6 for the consideration of absorption spectrum complementarity and cascade energetic alignment. In addition, the features of crystallinity and miscibility of the dual‐acceptor deliver optimized morphology lead to a high PCE of 18.84%. In addition, the asymmetric acceptor BTP‐S9 with a larger dipole moment shows tighter molecular stacking and longer crystal correlation length, which favor intrinsic molecular photostability, and further consolidate the operational lifetime of OSCs when coordinated with L8‐BO. This work demonstrates the efficacy of the SADA strategy for constructing efficient and stable OSCs.

Chemistry↗
DOE OSTI2021

Development of a kinetic model to describe six types of symbiotic interactions in a formate utilizing microalgae-bacteria cultivation system

This study investigated an algae/formate-utilizing-bacteria system that has been developed for carbon capture. This photomixotrophic consortium consumed formate to support bacterial growth so that the resulting respiration CO 2 could be simultaneously used by algae to avoid CO 2 gas-lipid mass transfer limitation. To understand biomass growth and population interactions in this unique system, a kinetic model has been developed to describe algae and bacteria multiplication, formate and nitrogen utilizations, CO 2 mass transfer, O 2 generation and consumption, lighting condition and shading effect. The simulation indicated that this ecosystem could form six types of interactions (mutualism, commensalism, parasitism, neutralism, amensalism, and competition) depending on the light intensity and nutrient availability. Furthermore, the simulation of both batch culture and chemostat, along with parameter sensitivity tests and experimental observations, offered insights into optimal applications of algae-bacteria consortium based photobiorefinery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Effect of high scandium doping in barium zirconate on nickel diffusion and performance of proton-conducting solid oxide electrolyzer cells

Proton-conducting solid oxide electrolyzer cells (p-SOECs) are emerging but promising technologies for hydrogen production. However, due to the lack of a robust electrolyte, p-SOECs struggle simultaneously to display high performance, Faradaic efficiency, and durability. Motivated by its high proton concentrations and stability as a barium zirconate, we have investigated BaZr 0.6 Sc 0.4 O 3-δ (BZSc40) as a potential next-generation p-SOEC electrolyte. Here, we found elevated levels of NiO diffusion through BZSc40 electrolytes during high-temperature sintering, attributed to the large oxygen vacancy concentrations present in BZSc40, as revealed by first-principle computational results. Controlling NiO diffusion is critical, as it can facilitate densification and grain size growth, but it may also detrimentally impact performance by causing electronic leakage. By optimizing sintering temperature when fabricating BZSc40 cells, we successfully controlled NiO diffusion, achieving sufficient electrolyte densification along with high performance and Faradaic efficiency. BZSc40 cells reached −0.99 A/cm 2 at 1.3 V and 600 °C and exhibited enhanced durability with a 3.37 mV/kh degradation rate at −0.8 A/cm 2 over a 200-h testing period. BZSc40 electrolytes demonstrated superior performance over BaZr 0.8 Y 0.2 O 3-δ (BZY20). In addition to elevated current densities and grain sizes, BZSc40 cells achieved Faradaic efficiencies of 76 % compared to 54 % for BZY20 at −0.2 A/cm 2 and 600 °C. This work lays the foundation for BZSc40 as a potential electrolyte due to its advantages over BZY20 while demonstrating the significance of controlling NiO diffusion when fabricating p-SOECs.

Electrolyzer↗
DOE OSTI2024

Symmetry breaking of fluorophore binding to a G-quadruplex generates an RNA aptamer with picomolar K D

Fluorogenic RNA aptamer tags with high affinity enable RNA purification and imaging. The G-quadruplex (G4) based Mango (M) series of aptamers were selected to bind a thiazole orange based (TO1-Biotin) ligand. Using a chemical biology and reselection approach, we have produced a MII.2 aptamer–ligand complex with a remarkable set of properties: Its unprecedented K D of 45 pM, formaldehyde resistance (8% v/v), temperature stability and ligand photo-recycling properties are all unusual to find simultaneously within a small RNA tag. Crystal structures demonstrate how MII.2, which differs from MII by a single A23U mutation, and modification of the TO1-Biotin ligand to TO1-6A-Biotin achieves these results. MII binds TO1-Biotin heterogeneously via a G4 surface that is surrounded by a stadium of five adenosines. Breaking this pseudo-rotational symmetry results in a highly cooperative and homogeneous ligand binding pocket: A22 of the G4 stadium stacks on the G4 binding surface while the TO1-6A-Biotin ligand completely fills the remaining three quadrants of the G4 ligand binding face. Similar optimization attempts with MIII.1, which already binds TO1-Biotin in a homogeneous manner, did not produce such marked improvements. We use the novel features of the MII.2 complex to demonstrate a powerful optically-based RNA purification system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Magnetostriction and temperature dependent Gilbert damping in boron doped Fe 80 ⁢Ga 20 thin films

Magnetic thin films with strong magnetoelastic coupling and low Gilbert damping are key materials for many magnetoelectric devices. Here, we investigated the effects of boron doping concentration on magnetostriction and temperature dependent Gilbert damping in magnetron sputtered (Fe 80 ⁢Ga 20 ) 1−𝑥 ⁢B 𝑥 films. A crystalline to amorphous structural transition was observed for a boron content near 8% and coincided with a decrease in coercivity from 76 Oe to 3 Oe. A 10% doping concentration is optimal for achieving both large magnetostriction of 48.8 ppm and low Gilbert damping of 6×10 −3 . The temperature dependence of the damping shows an increase at low temperatures with a peak around 40 K, and we associate the relative increase Δ⁢𝛼/𝛼 RT with magnetoelastic contributions to the damping, which has a maximum of 55.7% at 8% boron. An increase in the inhomogeneous linewidth broadening was observed in the structural transition regime at about 8% boron concentration. Furthermore, this study suggests that incorporation of glass forming elements, in this case boron, into Fe 80 ⁢Ga 20 is a practical pathway for simultaneously achieving enhanced magnetoelastic coupling and reduced Gilbert damping.

Cryogenics↗
DOE OSTI2021

Computational Fluid Dynamics Simulations to Support Efficiency Improvements in Aluminum Smelting Process

Smelting is broadly described as the extraction of a metal from its ore. In the United States, aluminum is commonly produced by smelting alumina in bauxite using the Hall-Héroult process. Optimization of equipment and processes in conventional smelting is crucial to enhancing process efficiency and productivity, is necessary for improving the techno-economic feasibility, which directly manifests as the growth of the American economy. To achieve optima, insightful data on the multiphysics phenomena that are inherent to the process must be obtained through physical investigation or high-fidelity numerical simulations. The resolution of relevant scales in time and space for smelting operations requires intensive, high-performance computing (HPC) simulations. Hostile operating conditions limit physical data acquisition to specific techniques; therefore, these data do not describe the multiscale interaction of simultaneous effects. Fortunately, in recent decades, significant advancements in computing hardware and computational methods have made the numerical resolution of such a complex process possible. In this study, a high-fidelity simulation of aluminum smelting was performed using an open-source tool, OpenFOAM, which analyzed many parameters characteristic to underlying phenomena. A multiphysics model based on the Eulerian-Eulerian multifluid approach was adopted. This model can resolve critical issues in the electrolytic smelting of aluminum, such as bubbling of carbon dioxide from the anode(s), magnetohydrodynamics from electromagnetic effects, ionic dissolution of the alumina in the electrolyte, and the evolution of thermal profiles. This study provides valuable connectivity for characteristic data that can direct the future designs of efficient smelters. A basic framework to model and simulate the smelting process using OpenFOAM is presented for user modification in keeping with process development. Of relevance to the flow field, a detailed investigation of vortices produced by bubble motion and electromagnetics is discussed, along with their impact on the evolution of thermal profiles. The predictions show small-scale vortices in the clearance between the anode and cathode caused by magnetic forces. Predictions also indicate relatively large-scale vortices in the inter-anode space resulting from carbon dioxide rising through the electrolytic flow field. The formation of vortices at the edges of anodes was shown to direct alumina charged by the feeder to the bottom of the anodes, thus preventing the entrapment of gas bubbles in the periphery of the bottom of the anode. Symmetry was observed in the location of cold spots in the electrolytic mixture in the vicinity of the feeder. Cold spots were also observed in the clearance between the anode and cathode due to the flow’s transmission of unconverted alumina to this region.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Integrated control of individual plasma scalars with simultaneous neoclassical tearing-mode suppression

A novel integrated-control architecture has been tested in nonlinear, one-dimensional simulations using the control-oriented transport simulator (COTSIM©) and in DIII-D experiments. Integrated architectures that can perform continuous-mission control while also handling off-normal events will be vital in future reactor-grade tokamaks. Continuous-mission controllers for individual magnetic and kinetic scalars (thermal stored-energy (W), volume-average toroidal rotation (Ω Φ ), and safety factor profile (q) at different spatial locations) have been integrated in this work with event-triggered neoclassical tearing-mode (NTM) suppression controllers by combining them into an architecture augmented by a supervisory and exception handling (S&EH) system and an actuator management (AM) system. Here, the AM system, which enables the integration of competing controllers, solves in real time a nonlinear optimization problem that takes into account the high-level control priorities dictated by the S&EH system. The resulting architecture offers a high level of integration and some of the functionalities that will be required to fulfill the advanced-control requirements anticipated for ITER. Initial simulations using COTSIM suggest that the plasma performance and its MHD stability may be improved under integrated feedback control. In addition, the integrated-control architecture has been implemented in the DIII-D plasma control system and tested experimentally for the first time ever in DIII-D in a high-q min scenario, which is a candidate for steady-state operation in ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

STSR-INR: Spatiotemporal super-resolution for multivariate time-varying volumetric data via implicit neural representation

Implicit neural representation (INR) has surfaced as a promising direction for solving different scientific visualization tasks due to its continuous representation and flexible input and output settings. We present STSR-INR, an INR solution for generating simultaneous spatiotemporal super-resolution for multivariate time-varying volumetric data. Inheriting the benefits of the INR-based approach, STSR-INR supports unsupervised learning and permits data upscaling with arbitrary spatial and temporal scale factors. Unlike existing GAN- or INR-based super-resolution methods, STSR-INR focuses on tackling variables or ensembles and enabling joint training across datasets of various spatiotemporal resolutions. Here we achieve this capability via a variable embedding scheme that learns latent vectors for different variables. In conjunction with a modulated structure in the network design, we employ a variational auto-decoder to optimize the learnable latent vectors to enable latent-space interpolation. To combat the slow training of INR, we leverage a multi-head strategy to improve training and inference speed with significant speedup. We demonstrate the effectiveness of STSR-INR with multiple scalar field datasets and compare it with conventional tricubic+linear interpolation and state-of-the-art deep-learning-based solutions (STNet and CoordNet).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems

Modern buildings are increasingly automated and often equipped with multiple building services (e.g., air-conditioning and mechanical ventilation (ACMV), dynamic shading, dimmable lighting). These systems are conventionally controlled individually without considering their interactions, affecting the building’s overall energy inefficiency and occupant comfort. A model predictive control (MPC) system that features a multi-objective MPC scheme to enable coordinated control of multiple building services for overall optimized energy efficiency, indoor thermal and visual comfort, as well as a hybrid model for predicting indoor visual comfort and lighting power is proposed. The MPC system was implemented in a test facility having two identical, side-by-side experimental cells to facilitate comparison with a building management system (BMS) employing conventional reactive feedback control. The MPC system coordinated the control of the ACMV, dynamic façade and automated dimmable lighting systems in one cell while the BMS controlled the building services in the other cell in a conventional manner. The MPC side achieved 15.1–20.7% electricity consumption reduction, as compared to the BMS side. Simultaneously, the MPC system improved indoor thermal comfort by maintaining the room within the comfortable range (-0.5 < predicted mean vote < 0.5) for 98.3% of the time, up from 91.8% of the time on the BMS side. Visual comfort, measured by indoor daylight glare probability (DGP) and horizontal illuminance level at work plane height, was maintained for the entire test period on the MPC side, improving from having visual comfort for 94.5% and 85.7% of the time, respectively, on the BMS side.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Perspectives on the relationship between materials chemistry and roll-to-roll electrode manufacturing for high-energy lithium-ion batteries

As lithium-ion battery (LIB) active material and cell manufacturing costs continue to drop with wider adoption of electric vehicles, electrode and cell processing costs remain too high in terms of reaching the ultimate U.S. Department of Energy (DOE) cell cost target of $80/kWh. This paper primarily covers major materials chemistry advancements made over the last 10 years at Oak Ridge National Laboratory (ORNL) in the space of advanced manufacturing science for LIBs with the aim of simultaneously meeting the ultimate cost target, 500 Wh/kg gravimetric energy density, 10-15-min fast charge times, and 1000 deep discharge cycles. Aqeous electrode processing with a variety of active anode and cathode materials is now a standard procedure at the DOE Battery Manufacturing R&D Facility at ORNL (BMF), including the latest processes developed for Ni-rich cathodes. New results on cobalt-free LiNi 0.8 Fe 0.1 Al 0.1 O 2 (NFA 811) are also included and discussed in an electrode processing advantage context. In addition, colloidal processing advancements have been made for Si/C composite anodes for achieving >600 mA h/g capacities. Optimization of electrode coating parameters and drying protocols have been completed, which has elucidated how key processing variables need to be changed when parameters such as slurry solids loading, solvent type, and wet electrode thickness are changed. ORNL has also increased the line speeds at which thick cathodes can be processed using ultra-fast electron beam (EB) curing and substantially decreased formation cycling times to <1 day. Finally, key details of these advancements are discussed in the context of materials chemistry and process-property-performance relationships.

25 ENERGY STORAGE↗
DOE OSTI2024

Using Convex Optimization to Efficiently Apportion Tracer and Pollutant Sources From Point Concentration Observations

Abstract Rivers transport elements, minerals, chemicals, and pollutants produced in their upstream basins. A sample from a river is a mixture of all of its upstream sources, making it challenging to pinpoint the contribution from each individual source. Here, we show how a nested sample design and convex optimization can be used to efficiently unmix downstream samples of a well‐mixed, conservative tracer in a steady state system into the contributions of their upstream sources. Our approach is significantly faster than previous methods. We represent the river's sub‐catchments, defined by sampling sites, using a directed acyclic graph. This graph is used to build a convex optimization problem which, thanks to its convexity, can be quickly solved to global optimality—in under a second on desktop hardware for data sets of ∼100 samples or fewer. Uncertainties in the upstream predictions can be generated using Monte Carlo resampling. We provide an open‐source implementation of this approach in Python. The inputs required are straightforward: a table containing sample locations and observed tracer concentrations, along with a D8 flow‐direction raster map. As a case study, we use this method to map the elemental geochemistry of sediment sources for rivers draining the Cairngorms mountains, UK. This method could be extended to non‐conservative and non‐steady state tracers. We also show, theoretically, how multiple tracers could be simultaneously inverted to recover upstream run‐off or erosion rates as well as source concentrations. Overall, this approach can provide valuable insights to researchers in various fields, including water quality, geochemical exploration, geochemistry, hydrology, and wastewater epidemiology.

Barnes, Richard↗
DOE OSTI2020

A novel approach for critical heat flux enhancement during severe accident mitigation with removal of radioactive materials from the coolant

In this paper, a new system, which consists of mature and proven technologies, is suggested for simultaneous objectives in nuclear power plants: CHF enhancement and effective decontamination. It consists of multifunctional magnetic nanoparticles and a collector part. CHF enhancement for severe accident mitigation strategies like IVR-ERVC and core catcher is expected along with an efficient decontamination process of target radioactive nuclides, and through the collector part, where external magnetic fields are applied, working fluid can be effectively purified. The multifunctional magnetic nanoparticle possesses potentials to severe accident scenarios based on its distinct characteristics. Also, a nanofluid system is strongly affected by environments and state of the immersed particles; the CHF enhancement can be stably achieved in real applications with proper consideration and optimization.

Kam, Dong Hoon↗
DOE OSTI2026

Engineering acid-base active sites on an N-doped biochar catalyst for microwave-assisted biodiesel production from waste cooking oil

Acid-base bifunctional catalysts play a crucial role in converting high-free fatty acid (FFA) feedstocks into biodiesel by enabling the simultaneous esterification and transesterification of triglycerides in a single step, thereby minimizing saponification and streamlining the process. In this study, we report the design and development of a novel N-doped acid-base bifunctional biochar catalyst synthesized from renewable biomass precursors- glucose and chitosan. The catalyst’s acidic sites were introduced via surface functionalization with o-sulfobenzoic acid cyclic anhydride, grafting -SO₃H groups onto the carbon framework, while basic sites were generated from amine functionalities (-NH) derived from chitosan, yielding a robust and truly bifunctional surface. The catalyst was characterized and used for the microwave-assisted transesterification of waste cooking oil, and process optimization using response surface methodology (RSM-CCD) yielding a maximum biodiesel yield of 98.8 ± 0.4% under optimal conditions (methanol-to-oil molar ratio 20:1, catalyst loading 5.7 wt%, temperature 99 °C, and reaction time 46.6 min). The catalyst demonstrated good stability, maintaining a yield of 92.2 ± 0.4% after eight reuse cycles. A Life Cycle Assessment (LCA) conducted for 1 kg of biodiesel production revealed a global warming potential of 0.85 kg CO 2 eq and human toxicity potential of 1.59 kg 1,4-DB eq, with the largest environmental contributions arising from catalyst preparation and reuse. In conclusion, this work highlights a green and circular approach to biodiesel production, combining renewable catalyst design, waste feedstock utilization, and quantitative sustainability assessment to guide future developments in sustainable catalysis and biofuel engineering.

Bifunctional catalyst↗