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Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective
We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass and renewable feedstocks, in general, is to produce new molecules and products with unmatched properties that have no analog in traditional refineries. Discovering such performance-advantaged molecules and the paths and processes to make them rapidly and systematically can transform manufacturing practices. Furthermore, we discuss retrosynthetic approaches, text mining, natural language processing, and modern machine learning methods to enable digitalization. Laboratory and multiscale computation automation via active learning are crucial to complement existing literature and expedite discovery and valuable data collection without a human in the loop. Such data can help process simulation and optimization select the most promising processes and molecules according to economic, environmental, and societal metrics. We propose the close integration between bench and process scale models and data to exploit the low dimensionality of the data and transform the manufacturing for renewable feedstocks.
Dynamical control in a prethermalized molecular ultracold plasma: Local dissipation drives global relaxation
Prethermalization occurs as an important phase in the dynamics of isolated many-body systems when coupled degrees of freedom relax in a subspace separated from a state of complete thermodynamic equilibrium by a gap in energy or other conserved quantity. Slow equilibration from a prethermal phase can localize the dynamics of natural and model systems despite high dimensionality and limited disorder. Here, we report the signature of an enduring prethermal regime of arrested relaxation in the molecular ultracold plasma that forms following the avalanche of a state-selected Rydberg gas of nitric oxide. For a wide range of initial conditions, this system enters a critical phase in which a density of NO + and electrons balances a population of Rydberg molecules. Electron collisions mix orbital angular momentum, scattering Rydberg electrons to states of very high ℓ . The rapid predissociation of molecules in states of low- ℓ purifies this high- ℓ ensemble. The angular momentum barrier that separates NO + ions and electrons creates an extraordinary gap between the plasma states of n ≈ ℓ , with measured n > 200 and penetrating states of ℓ = 0 , 1 , and 2. Evolution to a statistically equilibrated state of N and O atoms cannot occur without Rydberg electron penetration, and this gap blocks relaxation for a millisecond or more. A weak radiofrequency (RF) field drives ℓ -mixing electron collisions that erase this gap, causing wholesale dissipation. Remarkably, a local quantum-state transition promoting an exceedingly small fraction of the molecules in the prethermalized ensemble to a predissociative state also acts with a global effect, driving the entire system to a dissipative equilibrium. Using the Lindblad master equation, we illustrate qualitatively similar dynamics for a toy model of an open quantum system that consists of a localized set of spins on which dissipation acts at a single site as a gateway to equilibrium. Published by the American Physical Society 2025
Full Scale 3D Computational Model of the Industrial -Scale Coal Fired Boiler Performance for Temperature Sensor Installation Guidance
Abstract Nearly 30% of the electricity is generated by using coal as the primary fuel in the US. One of the major concerns in coal-fired power plants is the failure of boiler tubes that leads to unscheduled maintenance and has a huge economical and societal impact. High temperature flue gas along with ash pass over the boiler tubes, which over time leads to tube failure. Therefore, developing temperature sensors for harsh environments and install them for temperature sensing and boiler tube lifetime prediction is an urgent need. On the side of sensor development, the location of the sensor installation is important for stable sensing performance and easy calibration. In this study, computational fluid dynamics and heat transfer modeling are adopted to establish a full-scale 3-dimensional model of a coal-fired boiler to investigate the flue gas temperature distribution within the boiler and identify the proper locations for sensor installation. We proposed three criteria to select the temperature sensor installation location: (1) select the boiler tube panel away from the sidewalls, (2) select the boiler tube section closer to the top wall of the boiler; and (3) select the boiler tube on the back of the boiler panel (not directly facing the flue gas flow). In these regions, the flue gas temperature is stable, providing an ideal environment for stable temperature sensing and calibration.
Building two-dimensional materials one row at a time: Avoiding the nucleation barrier
Assembly of two-dimensional (2D) molecular arrays on surfaces produces a wide range of architectural motifs exhibiting unique properties, but little attention has been given to the mechanism by which they nucleate. Using peptides selected for their binding affinity to molybdenum disulfide, we investigated nucleation of 2D arrays by molecularly resolved in situ atomic force microscopy and compared our results to molecular dynamics simulations. The arrays assembled one row at a time, and the nuclei were ordered from the earliest stages and formed without a free energy barrier or a critical size. The results verify long-standing but unproven predictions of classical nucleation theory in one dimension while revealing key interactions underlying 2D assembly.
Nanoimprinted superlattice metallic photonic crystal as ultraselective solar absorber
Here, a two-dimensional superlattice metallic photonic crystal (PhC) and its fabrication by nanoimprint lithography on tantalum substrates are presented. The superior tailoring capacity of the superlattice PhC geometry is used to achieve spectrally selective solar absorption optimized for high-temperature and high-efficiency solar-energy-conversion applications. The scalable fabrication route by nanoimprint lithography allows for a high-throughput and high-resolution replication of this complex pattern over large areas. Despite the high fill factor, the pattern of polygonal cavities is accurately replicated into a resist that hardens under ultraviolet radiation over an area of 10 mm 2 . In this way, cavities of 905 nm and 340 nm width are achieved with a period of 1 μm. After pattern transfer into tantalum via a deep reactive ion-etching process, the achieved cavities are 2.2 μm deep, separated by 85–95 nm wide ridges with vertical sidewalls. The room-temperature reflectance spectra of the fabricated samples show excellent agreement with simulated results, with a high spectral absorptance approaching blackbody absorption in the range from 300 to 1900 nm and a steep cutoff. The calculated solar absorptivity of this superlattice PhC is 96% and its thermal transfer efficiency is 82.8% at an operating temperature of 1500 K and an irradiance of 1000 kW/m 2 .
Process Development for Selective Laser Melting of Molybdenum
This report presents a process development effort aimed at both identifying operating parameters for producing high quality Molybdenum components via Selective Laser Melting (SLM) and developing a framework for understanding parameter interactions and enabling informed decision-making in parameter selection. At the core of this effort was mapping of SLM process inputs to build outcomes, for which there are application-based constraints, e.g. final part density, surface finish, and dimensional accuracy. Process mapping in additive manufacturing is currently the subject of intense research efforts at many institutions, both experimentally and through computational modeling; this was an experimentally driven research effort, which was carried out with an understanding of the importance of the underlying mechanisms that are inherent to the SLM process, such as the impact of laser power and scan speed on melt pool morphology, as well as solidification dynamics. Results revealed promising avenues for mapping of SLM process parameters for Molybdenum and quantitatively reinforced ideas derived from observations of part quality, i.e. density, mass, surface roughness.
ORIGEN Integration into MPACT
This report documents the work performed to meet the objectives of the RTM milestone on ORIGEN Integration into MPACT. The main objective of the milestone was to demonstrate the capability to perform depletion problems with MPACT where the point depletion calculation, which solves the nuclide transmutation equation with ORIGEN. While MPACT already has a depletion capability based on ORIGEN, it is desired to transition to using ORIGEN because ORIGEN has undergone far more validation and software quality assurance than the implementation in MPACT. The integration was completed by compiling and linking the two codes together at build-time. Within the TriBITS build structure used for VERA, the MPACT and SCALE code repositories are already present. An optional dependency was created between MPACT to SCALE. The API available within ORIGEN was modified slightly to be able to be used by MPACT and two new software classes were implemented in MPACT to call the necessary ORIGEN subroutines. To test the ORIGEN integration two benchmark problems were analyzed. The first being the spent fuel isotopics from the Takahama-3 PWR reactor. This involved comparisons to experimentally measured nuclide concentrations for select actinides and fission products, and comparisons to other numerical results. The benchmark defines a 1-D problem limiting most of the effects due to the accuracy of a multi-dimensional transport solver, and thus differences can be assumed to be more likely caused by the point depletion solver methodology and associated nuclear data. The other benchmark analyzed two problems from the reactor physics benchmarks for LWR next generation fuel. The two configurations simulated were a PWR UO 2 pin cell and PWR UO 2 assembly. For all the benchmark problems the results were generally in good agreement with the other numerical results which were computed by several codes, including MPACT with its original depletion implementation. Thus it is concluded that ORIGEN was successfully integrated into MPACT and is capable of providing reasonable results. An additional cursory analysis of the performance of the ORIGEN point depletion compared to native MPACT point depletion solver was also performed. In this analysis it was observed that ORIGEN, on average, needs about 0.28 to 0.30 seconds to perform the point depletion calculation. This is greater than the target performance of 0.1 seconds which means future work to investigate smaller depletion chains will be needed. The MPACT internal depletion solver completes the point depletion calculation in 0.02 seconds, so it may be used for routine analysis until the use of a simplified burnup chain and performance improvements are implemented. Other future work should focus on implementing the post-correction fix-up and quadratic depletion and computation of decay heat and gamma sources.
Prospecting Biotechnologically-Relevant Monooxygenases from Cold Sediment Metagenomes: An In Silico Approach
The goal of this work was to identify sequences encoding monooxygenase biocatalysts with novel features by in silico mining an assembled metagenomic dataset of polar and subpolar marine sediments. The targeted enzyme sequences were Baeyer–Villiger and bacterial cytochrome P450 monooxygenases (CYP153). These enzymes have wide-ranging applications, from the synthesis of steroids, antibiotics, mycotoxins and pheromones to the synthesis of monomers for polymerization and anticancer precursors, due to their extraordinary enantio-, regio-, and chemo- selectivity that are valuable features for organic synthesis. Phylogenetic analyses were used to select the most divergent sequences affiliated to these enzyme families among the 264 putative monooxygenases recovered from the ~14 million protein-coding sequences in the assembled metagenome dataset. Three-dimensional structure modeling and docking analysis suggested features useful in biotechnological applications in five metagenomic sequences, such as wide substrate range, novel substrate specificity or regioselectivity. Further analysis revealed structural features associated with psychrophilic enzymes, such as broader substrate accessibility, larger catalytic pockets or low domain interactions, suggesting that they could be applied in biooxidations at room or low temperatures, saving costs inherent to energy consumption. This work allowed the identification of putative enzyme candidates with promising features from metagenomes, providing a suitable starting point for further developments.
CLFE2D: A generalized plane strain finite element program laminated composites subject to mechanical and hygrothermal loading
CLFE2D is a two dimensional generalized plane strain finite element code, using a linear, four node, general quadrilateral, isoparametric element. The program is developed to calculate the displacements, strains, stresses, and strain energy densities in a finite width composite laminate. CLFE2D offers any combination of the following load types: nodal displacements, nodal forces, uniform normal strain, or hygrothermal. The program allows the user to input one set of three dimensional orthotropic material properties. The user can then specify the angle of material principal orientation for each element in the mesh. Output includes displacements, stresses, strains and strain densities at points selected by the user. An option is also available to plot the underformed and deformed finite element meshes.
Investigations of Clouds and Aerosols on Mars, Venus and Titan
This final report was included as part of a new proposal. This new proposal was selected for funding on 9 Nov. 1999. For Titan our interest during the past few years was to explain the observed asymmetry in the albedo. We suggested earlier, from one-dimensional modeling studies, that vertical transport rates were comparable to particle fall speeds. Since heating of the upper atmosphere, which drives dynamical motions, is largely due to the aerosols, a nonlinear interaction between dynamics, radiative heating and particle microphysics is possible. We pursued this interaction in a two-dimensional model. We showed that the observed variations in the albedo between the two hemispheres and over an orbital cycle, could be due to dynamical motions suspending particles so that particle sizes and optical depths vary across the planet. In the Cassini time frame, future studies of this interaction between dynamics, radiation and microphysics may be worthwhile using, the strong modeling base that others, and we have developed. In our recently approved proposal, however, we plan to extend our modeling to hydrocarbon clouds that lie at lower levels. We know very little about such clouds, and numerical models for their properties are non-existent. These clouds may be observed by the Huygen's Probe, and by the Cassini orbiter, so predictions of their properties should help in the analysis of Cassini data. We have also developed a sophisticated model for the lower, condensational, clouds on Venus. In this model we explored the water vapor budget on Venus, and the properties of the clouds such as particle size distribution. During the past few years we have applied our model of the water ice clouds on Mars to new data sets from Pathfinder, and Mars Global Surveyor. We have compared our predictions of cloud properties with those seen by Pathfinder, and found reasonable agreement.
NASA GPM GV Science Requirements
An important scientific objective of the NASA portion of the GPM Mission is to generate quantitatively-based error characterization information along with the rainrate retrievals emanating from the GPM constellation of satellites. These data must serve four main purposes: (1) they must be of sufficient quality, uniformity, and timeliness to govern the observation weighting schemes used in the data assimilation modules of numerical weather prediction models; (2) they must extend over that portion of the globe accessible by the GPM core satellite to which the NASA GV program is focused - (approx.65 degree inclination); (3) they must have sufficient specificity to enable detection of physically-formulated microphysical and meteorological weaknesses in the standard physical level 2 rainrate algorithms to be used in the GPM Precipitation Processing System (PPS), i.e., algorithms which will have evolved from the TRMM standard physical level 2 algorithms; and (4) they must support the use of physical error modeling as a primary validation tool and as the eventual replacement of the conventional GV approach of statistically intercomparing surface rainrates fiom ground and satellite measurements. This approach to ground validation research represents a paradigm shift vis-&-vis the program developed for the TRMM mission, which conducted ground validation largely as a statistical intercomparison process between raingauge-derived or radar-derived rainrates and the TRMM satellite rainrate retrievals -- long after the original satellite retrievals were archived. This approach has been able to quantify averaged rainrate differences between the satellite algorithms and the ground instruments, but has not been able to explain causes of algorithm failures or produce error information directly compatible with the cost functions of data assimilation schemes. These schemes require periodic and near-realtime bias uncertainty (i.e., global space-time distributed conditional accuracy of the retrieved rainrates) and local error covariance structure (i.e., global space-time distributed error correlation information for the local 4-dimensional space-time domain -- or in simpler terms, the matrix form of precision error). This can only be accomplished by establishing a network of high quality-heavily instrumented supersites selectively distributed at a few oceanic, continental, and coastal sites. Economics and pragmatics dictate that the network must be made up of a relatively small number of sites (6-8) created through international cooperation. This presentation will address some of the details of the methodology behind the error characterization approach, some proposed solutions for expanding site-developed error properties to regional scales, a data processing and communications concept that would enable rapid implementation of algorithm improvement by the algorithm developers, and the likely available options for developing the supersite network.
High surface area graphene-supported metal chalcogenide assembly
A composition comprising at least one graphene-supported assembly, which comprises a three-dimensional network of graphene sheets crosslinked by covalent carbon bonds, and at least one metal chalcogenide compound disposed on said graphene sheets, wherein the chalcogen of said metal chalcogenide compound is selected from S, Se and Te. Also disclosed are methods for making and using the graphene-supported assembly, including graphene-supported MoS.sub.2. Monoliths with high surface area and conductivity can be achieved. Lower operating temperatures in some applications can be achieved. Pore size and volume can be tuned.
PREDICTIONS OF ION PRODUCTION RATES AND ION NUMBER DENSITIES WITHIN THE DIAMAGNETIC CAVITY OF COMET 67P/CHURYUMOV-GERASIMENKO AT PERIHELION
We present a one-dimensional ion chemistry model of the diamagnetic cavity of comet 67P/Churyumov-Gerasimenko, the target comet for the ESA Rosetta mission. We solve the continuity equations for ionospheric species and predict number densities of electrons and selected ions considering only gas-phase reactions. We apply the model to the subsolar direction and consider conditions expected to be encountered by Rosetta at perihelion (1.29 AU) in 2015 August. Our default simulation predicts a maximum electron number density of {approx}8 Multiplication-Sign 10{sup 4} cm{sup -3} near the surface of the comet, while the electron number densities for cometocentric distances r > 10 km are approximately proportional to 1/r {sup 1.23} assuming that the electron temperature is equal to the neutral temperature. We show that even a small mixing ratio ({approx}0.3%-1%) of molecules having higher proton affinity than water is sufficient for the proton transfer from H{sub 3}O{sup +} to occur so readily that other ions than H{sub 3}O{sup +}, such as NH{sub 4} {sup +} or CH{sub 3}OH{sub 2} {sup +}, become dominant in terms of volume mixing ratio in part of, if not throughout, the diamagnetic cavity. Finally, we test how the predicted electron and ion densities are influenced by changes of model input parameters, including the neutral background, the impinging EUV solar spectrum, the solar zenith angle, the cross sections for photo- and electron-impact processes, the electron temperature profile, and the temperature dependence of ion-neutral reactions.
A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks
To fit sparse linear associations, a LASSO sparsity inducing penalty with a single hyperparameter provably allows to recover the important features (needles) with high probability in certain regimes even if the sample size is smaller than the dimension of the input vector (haystack). More recently learners known as artificial neural networks (ANN) have shown great successes in many machine learning tasks, in particular fitting nonlinear associations. Small learning rate, stochastic gradient descent algorithm and large training set help to cope with the explosion in the number of parameters present in deep neural networks. Yet few ANN learners have been developed and studied to find needles in nonlinear haystacks. Driven by a single hyperparameter, our ANN learner, like for sparse linear associations, exhibits a phase transition in the probability of retrieving the needles, which we do not observe with other ANN learners. To select our penalty parameter, we generalize the universal threshold of Donoho and Johnstone (Biometrika 81(3):425–455, 1994) which is a better rule than the conservative (too many false detections) and expensive cross-validation. In the spirit of simulated annealing, we propose a warm-start sparsity inducing algorithm to solve the high-dimensional, non-convex and non-differentiable optimization problem. We perform simulated and real data Monte Carlo experiments to quantify the effectiveness of our approach.
Decoupling plasma, catalyst, and gaseous mechanisms for non-oxidative methane conversion
Direct non-oxidative methane (CH 4 ) conversion to value-added hydrogen (H 2 ) and C 2 products remains hindered by fundamental catalytic scaling constraints and rapid surface deactivation at elevated temperatures. Plasma-enabled catalysis offers a promising route to overcome the thermodynamic and kinetic barriers of direct non-oxidative methane upgrading at mild conditions, yet control over C–C product selectivity and catalyst stability remains elusive. Here, we establish a unified mechanistic framework including Langmuir–Hinshelwood (L–H) and Langmuir–Rideal (L–R) mechanisms that disentangles the roles of plasma excitation (including vibrationally activated species and radicals), surface temperature (T sur ), and catalyst binding energy in steering CH 4 conversion to H 2 and C 2 hydrocarbons. Through a combination of density functional theory (DFT) informed microkinetic modeling, in situ and ex situ surface characterization, and product quantification under dielectric barrier discharge conditions, we show that vibrationally excited CH 4 lowers activation barriers selectively for dissociative chemisorption, enabling surface activation across a wide range of transition metal catalysts at low thermal energy input. We find that once CH 4 is dissociatively chemisorbed, the branching between C 2 H 2 , C 2 H 4 , and C 2 H 6 is governed by surface properties (carbon binding energy, T sur , etc), regardless of plasma excitation. The DFT informed microkinetic model decouples the effects of molecular activation from surface properties and indentifies operating windows that maximize target yields while suppressing carbon accumulation and subsequent catalytic inactivation. Experiments on polycrystalline Cu/Al 2 O 3 , Ni/Al 2 O 3 , and Pt/Al 2 O 3 validate these predictions, revealing catalyst-dependent branching toward ethane or ethylene and distinct deactivation profiles. We unify these trends into a generalized three-dimensional plasma-thermal-catalytic design space, from which reduced descriptors such as T vib /T sur in the limit of vibrationally excited L–H pathways emerge as predictive metrics. These results enable rational tuning of methane conversion pathways and unlock selective C 2 formation using earth-abundant metals under mild plasma conditions.
Non-intrusive reduced-order modeling for dynamical systems with spatially localized features
This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed approach builds upon two existing methodologies for reduced and full-order non-intrusive modeling, namely Operator Inference (OpInf) and sparse Full-Order Model (sFOM) inference. We decompose the domain into two complementary subdomains that exhibit fast and slow singular value decay. The dynamics of the subdomain exhibiting slow singular value decay are learned with sFOM while the dynamics with intrinsically low dimensionality on the complementary subdomain are learned with OpInf. The resulting, coupled OpInf-sFOM formulation leverages the computational efficiency of OpInf and the high resolution of sFOM, and thus enables fast non-intrusive predictions for conditions beyond those sampled in the training data set. A novel regularization technique with a closed-form solution based on the Gershgorin disk theorem is introduced to promote stable sFOM and OpInf models. We also provide a data-driven indicator for subdomain selection and ensure solution smoothness over the interface via a post-processing interpolation step. We evaluate the efficiency of the approach in terms of offline and online speedup through a quantitative, parametric computational cost analysis. We demonstrate the coupled OpInf-sFOM formulation for two test cases: a one-dimensional Burgers’ model for which accurate predictions beyond the span of the training snapshots are presented, and a two-dimensional parametric model for the Pine Island Glacier ice thickness dynamics, for which the OpInf-sFOM model achieves an average prediction error on the order of 1% with an online speedup factor of approximately 8$\times$ compared to the numerical simulation.
Hetero-junction photovoltaic device and method of fabricating the device
A hetero-junction device and fabrication method in which phase-separated n-type and p-type semiconductor pillars define vertically-oriented p-n junctions extending above a substrate. Semiconductor materials are selected for the p-type and n-type pillars that are thermodynamically stable and substantially insoluble in one another. An epitaxial deposition process is employed to form the pillars on a nucleation layer and the mutual insolubility drives phase separation of the materials. During the epitaxial deposition process, the orientation is such that the nucleation layer initiates propagation of vertical columns resulting in a substantially ordered, three-dimensional structure throughout the deposited material. An oxidation state of at least a portion of one of the p-type or the n-type semiconductor materials is altered relative to the other, such that the band-gap energy of the semiconductor materials differ with respect to stoichiometric compositions and the device preferentially absorbs particular selected bands of radiation.