Selective Separation of the Rare Earth Elements Dysprosium and Neodymium via Tailoring Nanocellulose Chemical Structure
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Koshani, Roya [Department of Chemical Engineering ↗
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Data for Resourceful and Economical Designing of Fermentation Medium for Lab and Commercial Strains of Yeast from Alternative Feedstock: Transgenic Oilcane
Sugarcane plant engineered to accumulate lipids in its vegetative tissue is being developed as a new bioenergy crop. The new crop would be a source of juice, oil, and cellulosic sugars. However, limited tolerance of industrially recognized yeasts towards inhibitors generated during the processing of lignocellulosic biomass to produce fermentable sugars is a major challenge in developing scalable processes for second-generation drop-in fuel production. To this end, hydrolysates generated from engineered sugarcane—‘oilcane’ bagasse contain added phenolics and fatty acids that further restrict the growth of fermenting microorganisms and necessitate nutrient supplementation and/or detoxification of hydrolysate which makes the fermentation process expensive. Herein, we propose a resourceful and economical approach for growing lab and commercial strains of S. cerevisiae on unrefined cellulosic sugars aerobically and fermentatively. An equal ratio of hydrolysate and juice was found optimum for growth and fermentation by lab and commercial strains of Saccharomyces cerevisiae engineered for xylose fermentation. The industrial strain grew and fermented efficiently under low aeration conditions having an ethanol titer, yield, specific and volumetric productivities of 46.96 ± 0.19 g/l, 0.51 ± 0.00 g/g, 0.27 ± 0.02 g/g.h and 1.95 ± 0.01 g/l.h, respectively, while the lab strain grew better under higher aeration conditions having the ethanol titer, yield, specific and volumetric productivities of 24.93 ± 0.09, 0.27 ± 0.00 g/g, 0.17 ± 0.00 g/g.h and 1.04 ± 0.00 g/l.h, respectively. Acclimation of cultures in a blended medium significantly improved the performance of the yeast strains. The addition of transgenic oilcane juice, which is inedible and rich in amino acids, to the hydrolysate averted the need for expensive nutrient supplementation and detoxification steps of hydrolysate. The approach provides an economical solution to reduce the cost of fermentation at an industrial scale for second-generation drop-in fuel production.
Biomass Analytics↗
Large‐Scale 2D Perovskite Nanocrystals Photodetector Array via Ultrasonic Spray Synthesis
Abstract 2D perovskite (PVSK) single crystals have received significant attention due to their unique optical and optoelectronic properties. However, current synthesis methods face limitations, particularly in large‐area fabrication, which remain critical barriers to practical applications. In this study, the synthesis of red/green/purple‐blue‐colored 2D PVSK nanocrystals over a large area (4‐inch wafer) and the fabrication of high‐performance photodetector arrays are presented via a facile yet efficient spray‐coating approach with a liquid‐bridge transport effect. The photodetector array achieves 100% working yield, high photo‐responsivity (1.5 × 10 6 A W −1 ) and specific‐detectivity (1.1 × 10 16 Jones) with competitive photomapping characteristics. An intelligent vision system for automatic shape recognition is further demonstrated with a recognition rate exceeding 90%. This study provides significant advances in the scalable synthesis of nanoscale 2D PVSK crystals, their integration into large‐area optoelectronic devices, and their potential use in artificial‐intelligence systems.
Lee, Yoon Ho [Davidson School of Chemical Engineer↗
A comparison of the dielectric function for p-point water models
We compute the longitudinal dielectric function ɛl(k) for p-point water models, p = 3 (SPC/E, TIP3P), p = 4 (TIP4P-EW, OPC), and p = 5 (TIP5P-E) from the charge–charge fluctuation function SZZ(k), obtained from two methods: average of the charge in k-space (method:sum) and from the pair distribution function in Fourier space (method:integral). The latter requires a continuation to small k-values, which we thoroughly discuss. We conclude with a detailed comparison of the longitudinal dielectric function for the different p-point models and its main characteristics: small k and large k limits, position of the poles, and its (complex) zeros.
Shevy, Noah [Ames National Laboratory, Department ↗
Data for "Integrated Green Biorefinery for the Production of Anthocyanins, Fermentable Sugars, and High Pure Lignin from Miscanthus × giganteus "
Miscanthus x giganteus (Mxg) is a promising perennial crop for producing natural colorants, renewable fuels, and bioproducts. However, natural recalcitrance and high pretreatment cost are major barriers to their complete conversion. In this study, a green processing method has been investigated for efficient recovery of natural pigments (anthocyanins), fermentable sugars, and pure lignin from Mxg genotypes using choline chloride-based natural deep eutectic solvents (NADES) systems. Interestingly, choline chloride: lactic acid (ChCl: LA) NADES-processed biomass resulted in 67.8 ± 2.1 μg g−1 of anthocyanins from dry biomass. A maximum of 87.4%–94.1% glucose yield was achieved after enzymatic saccharification. The effective extraction of lignin with high purity with higher β-aryl ether (βO4) bonds from advanced crops is crucial for lignin valorization. Notably, highly pure lignin (≈93.4% ± 1.4%) is achieved after low-temperature NADES pretreatment while retaining lignin’s native structure. 31P nuclear magnetic resonance demonstrated that total phenolics for ChCl: LA-lignin resulted in 1.20 mmol g−1 hydroxyls. The relative monolignol composition of syringyl (S), guaiacyl (G), and p-hydroxyphenyl (H) is 19.0, 65.7, and 14.3%, respectively, as evidenced by heteronuclear single quantum coherence analysis. This study provides a novel approach for obtaining high-purity lignin for catalytic depolymerization for oligomers and bifunctional monoaromatics production and leverages current cellulosic biorefinery technologies.
biomass analytics↗
Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models
Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.
Kim, Seongmin [Department of Chemical and Biologic↗
Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models
Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.
Kim, Seongmin [Department of Chemical and Biologic↗
Data for "Anti-Pdc1p Nanobody as a Genetically Encoded Inhibitor of Ethanol Production Enables Dual Transcriptional and Post-translational Controls of Yeast Fermentations"
Microbial fermentation provides a sustainable method of producing valuable chemicals. Adding dynamic control to fermentations can significantly improve titers, but most systems rely on transcriptional controls of metabolic enzymes, leaving existing intracellular enzymes unregulated. This limits the ability of transcriptional controls to switch off metabolic pathways, especially when metabolic enzymes have long half-lives. We developed a two-layer transcriptional/post-translational control system for yeast fermentations. Specifically, the system uses blue light to transcriptionally activate the major pyruvate decarboxylase PDC1 , required for cell growth and concomitant ethanol production. Switching to darkness transcriptionally inactivates PDC1 and instead activates the anti-Pdc1p nanobody, NbJRI, to act as a genetically encoded inhibitor of Pdc1p accumulated during the growth phase. This dual transcriptional/post-translational control improves the production of 2,3-BDO and citramalate by up to 100 and 92% compared to using transcriptional controls alone in dynamic two-phase fermentations. This study establishes the NbJRI nanobody as an effective genetically encoded inhibitor of Pdc1p that can enhance the production of pyruvate-derived chemicals.
metabolic engineering↗
Measurement of Contact Resistivity In Symmetric Polycrystalline Si/SiO x /Monocrystalline Si Test Structures Using Variable Light Illumination
While different methods exist to determine the contact resistivity in semiconductor devices, these methods are limited to measurement of the majority carrier contacts. The measurement of p‐ or n‐type contacts on n‐ or p‐type crystalline silicon, respectively, is challenging due to the blocking diode formed by the p–n junction. In this article, we address this problem for tunneling oxide passivating contacts used in high‐efficiency Si solar cells. We propose a universal method to extract contact resistivity on symmetric test structures with polycrystalline silicon on SiO x (poly‐Si/SiO x ) passivating contacts under illumination, both for p–n and high–low‐junction passivated contacts. In this method, we demonstrate that the total contact resistance of each cell grid finger to the base wafer is governed by its effective contact area, defined by the transfer length extending from both sides of the finger. Therefore, the grid contact resistance of a poly‐Si contact depends on the ratio of the doped poly‐Si sheet resistance to the tunneling contact resistivity.
14 SOLAR ENERGY↗
Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing
Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.
AI/ML↗
Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing
Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.
Agricultural Sciences↗
CO 2 in Ionene–Ionic Liquid Composite Membranes
Abstract Ionene – ionic liquid (IL) composites are promising materials for CO 2 separation, yet a molecular‐level understanding of their structure and its impact on CO 2 speciation, solubility, rotation, and diffusivity remains unclear. Herein, using multimodal nuclear magnetic resonance (NMR), time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS), atomic force microscopy (AFM), and molecular dynamics (MD) simulations, we reveal that the composites contain IL‐rich domains extending across hundreds of nanometres within the ionene matrix, and these bicontinuous domains span the entire membrane depth. CO 2 also absorbs into the ionene matrix, with the distribution between two CO 2 species varying with temperature and time. The rotational correlation times of these two species are on the timescale of 0.1 and 1 ns, respectively. As IL content increases, the ionic domains expand, resulting in higher CO 2 solubility due to enhanced molecular dynamics and increased free volume in both ionene backbones and IL‐rich regions. Although CO 2 diffusion in the membranes is an order of magnitude slower than in bulk IL, the activation energy for CO 2 diffusion remains comparable. Ionene‐IL composites represent a promising platform for designing CO 2 separation membranes, offering enhanced CO 2 diffusion and selectivity through IL‐rich domains, and increased CO 2 solubility and mechanical integrity from the ionene matrix.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review
In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.
dynamic analysis↗
Data for "Evaluating the industrial potential of emerging biomass pretreatment technologies in bioethanol production and lipid recovery from transgenic sugarcane"
The selection of pretreatment methods is critical to achieving high product yields during bioconversion of lignocellulosic biomass. Hydrothermal, soaking-in-aqueous ammonia, and ionic liquid pretreatment methods are viable candidates for minimizing sugar decomposition, permitting the effective hydrolysis of structural carbohydrates, and producing a fermentable substrate suitable for achieving industrial ethanol titers and yields. In this study, the effect of these three pretreatment methods on non-modified sugarcane cultivar CP88-1762 and two transgenic lipid-accumulating sugarcane lines, oilcane 1565 and oilcane 1566, were investigated and compared in terms of lipid recovery, sugar yield, and ethanol yields within the lignocellulosic biomass conversion pipeline. Fed-batch enzymatic hydrolysis at high solid loading yielded hydrolysates capable of supporting industrial bioethanol titers across all conditions. The highest sugar yields were obtained on ammonia-pretreated biomass hydrolysate (253.73 g L−1), followed by hydrothermally pretreated hydrolysate (213.10 g L−1) and ionic liquid-pretreated hydrolysate (154.20 g L−1). Commercially viable ethanol titers of 100.62 g L−1, 64.47 g L−1, and 52.95 g L−1 were achieved from ammonia, hydrothermal, and ionic liquid pretreated hydrolysate with the corresponding ethanol productivities of 2.08 g L−1 h−1, 0.53 g L−1 h−1, and 0.36 g L−1 h−1. The lower acetic acid concentration in ammonia-pretreated hydrolysate may have enhanced its fermentability relative to the hydrothermal pretreatment condition, as indicated by the differences in ethanol titer and productivity. Lower sugar yields and ethanol productivities under the ionic liquid conditions likely resulted from the inhibitory effect of cholinium lysinate. Oilcane 1565 and oilcane 1566 bagasse accumulated over 16- and 3 times higher lipids than the non-modified sugarcane CP88-1762. The total fatty acid content in the oilcane samples was reduced in ammonia and ionic liquid-pretreated bagasse relative to the hydrothermal pretreatment condition. While all pretreatment techniques tested are industrially viable, the observed differences in titer, productivity, and lipid content indicate that careful selection and validation of upstream processing methods can contribute to improved economic and environmental outcomes.
biomass analytics↗
Systems Biology to Enable Modular Metabolic Engineering of Fatty Acid Production in Cyanobacteria
This final technical report synthesizes major research results for DOE award DE-SC0022207. The project sought to identify metabolic control points and bottlenecks that regulate flux to free fatty acids (FFAs) in Picosynechococcus sp. PCC 7002 by treating lipid metabolism as modular pathways upstream and downstream of acetyl-CoA.
09 BIOMASS FUELS↗
Enhancing 2‐Pyrone Synthase Efficiency by High‐Throughput Mass‐Spectrometric Quantification and In Vitro/In Vivo Catalytic Performance Correlation
Abstract Engineering efficient biocatalysts is essential for metabolic engineering to produce valuable bioproducts from renewable resources. However, due to the complexity of cellular metabolic networks, it is challenging to translate success in vitro into high performance in cells. To meet such a challenge, an accurate and efficient quantification method is necessary to screen a large set of mutants from complex cell culture and a careful correlation between the catalysis parameters in vitro and performance in cells is required. In this study, we employed a mass‐spectrometry based high‐throughput quantitative method to screen new mutants of 2‐pyrone synthase (2PS) for triacetic acid lactone (TAL) biosynthesis through directed evolution in E. coli . From the process, we discovered two mutants with the highest improvement (46 fold) in titer and the fastest k cat (44 fold) over the wild type 2PS, respectively, among those reported in the literature. A careful examination of the correlation between intracellular substrate concentration, Michaelis‐Menten parameters and TAL titer for these two mutants reveals that a fast reaction rate under limiting intracellular substrate concentrations is important for in‐cell biocatalysis. Such properties can be tuned by protein engineering and synthetic biology to adopt these engineered proteins for the maximum activities in different intracellular environments.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Synthetic soils for ecological and synthetic biology applications
Soils are heterogeneous and dynamic systems characterized by complex physical, chemical, and biological interactions. Understanding these interactions is critical, as they influence plant productivity, global biogeochemical cycles, and ecosystem resilience. While ecologists have long studied soils in field, greenhouse, and laboratory settings, their complexity and heterogeneity make it challenging to pinpoint key properties driving biological processes and derive mechanistic insights. Advancements in synthetic biology, which seeks to engineer and control biological processes in soils, have increased the demand for standardized and controllable experimental platforms. These platforms, referred to here as ‘synthetic soils’, are systems designed to reproduce selected physicochemical characteristics of natural soils in a simplified and defined format, allowing scientists to systematically change soil physicochemical properties (i.e. texture, mineralogy, pH) to study how biological components (i.e. microbes, plants, soil fauna, etc.) respond to, modify, or interact within these controlled environments. This review explores existing synthetic soils, their advantages, limitations, and applications in ecology and synthetic biology, and discusses potential directions for their future development.
artificial soils↗