Advancing Rare-Earth Separation by Machine Learning.
Ontology highlight
ABSTRACT:
SUBMITTER: Liu T
PROVIDER: S-EPMC9241157 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Advanced materials characterization techniques with ever-growing data acquisition speed and storage capabilities represent a challenge in modern materials science, and new procedures to quickly assess and analyze the data are needed. Machine learning approaches are effective in reducing the complexity of data and rapidly homing in on the underlying trend in multi-dimensional data. Here, we show that by employing an algorithm called the mean shift theory to a large amount of diffraction data in high-throughput experimentation, one can streamline the process of delineating the structural evolution across compositional variations mapped on combinatorial libraries with minimal computational cost. Data collected at a synchrotron beamline are analyzed on the fly, and by integrating experimental data with the inorganic crystal structure database (ICSD), we can substantially enhance the accuracy in classifying the structural phases across ternary phase spaces. We have used this approach to identify a novel magnetic phase with enhanced magnetic anisotropy which is a candidate for rare-earth free permanent magnet.
| S-EPMC4163667 | biostudies-literature
Project description:Rising global population and climate change realities dictate that agricultural productivity must be accelerated. Results from current traditional research approaches are difficult to extrapolate to all possible fields because they are dependent on specific soil types, weather conditions, and background management combinations that are not applicable nor translatable to all farms. A method that accurately evaluates the effectiveness of infinite cropping system interactions (involving multiple management practices) to increase maize and soybean yield across the US does not exist. Here, we utilize extensive databases and artificial intelligence algorithms and show that complex interactions, which cannot be evaluated in replicated trials, are associated with large crop yield variability and thus, potential for substantial yield increases. Our approach can accelerate agricultural research, identify sustainable practices, and help overcome future food demands.
| S-EPMC8429560 | biostudies-literature
Project description:To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.
| S-EPMC11231231 | biostudies-literature
Project description:Solvent extraction is used widely for chemical separations and environmental remediation. Although the kinetics and efficiency of this process rely upon the formation of ion-extractant complexes, it has proven challenging to identify the location of ion-extractant complexation within the solution and its impact on the separation. Here, we use tensiometry and X-ray scattering to characterize the surface of aqueous solutions of lanthanide chlorides and the water-soluble extractant bis(2-ethylhexyl) phosphoric acid (HDEHP), in the absence of a coexisting organic solvent. These studies restrict ion-extractant interactions to the aqueous phase and its liquid-vapor interface, allowing us to explore the consequences that one or the other is the location of ion-extractant complexation. Unexpectedly, we find that light lanthanides preferentially occupy the liquid-vapor interface. This contradicts our expectation that heavy lanthanides should have a higher interfacial density since they are preferentially extracted by HDEHP in solvent extraction processes. These results reveal the antagonistic role played by ion-extractant complexation within the aqueous phase and clarify the advantages of complexation at the interface. Extractants in common use are often soluble in water, in addition to their organic phase solubility, and similar effects to those described here are expected to be relevant to a variety of separations processes.
| S-EPMC8614105 | biostudies-literature
Project description:Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and "transduce" signals to adjust internal processes. We hypothesized that an ecosystem's unique stimuli leave a sensor "fingerprint," able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collection's nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient's disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.IMPORTANCEMicrobes infect, colonize, and proliferate due to their ability to sense and respond quickly to their surroundings. In this research, we extract the sensory proteins from a diverse range of environmental, engineered, and host-associated metagenomes. We trained machine learning classifiers using sensors as features such that it is possible to predict the ecosystem for a metagenome from its sensor profile. We use the optimized model's feature importance to identify the most impactful and predictive sensors in different environments. We next use the sensor profile from human gut metagenomes to classify their disease states and explore which sensors can explain differences between diseases. The sensors most predictive of environmental labels here, most of which correspond to uncharacterized proteins, are a useful starting point for the discovery of important environment signals and the development of possible diagnostic interventions.
| S-EPMC10804942 | biostudies-literature
Project description:The field of polymer membrane design is primarily based on empirical observation, which limits discovery of new materials optimized for separating a given gas pair. Instead of relying on exhaustive experimental investigations, we trained a machine learning (ML) algorithm, using a topological, path-based hash of the polymer repeating unit. We used a limited set of experimental gas permeability data for six different gases in ~700 polymeric constructs that have been measured to date to predict the gas-separation behavior of over 11,000 homopolymers not previously tested for these properties. To test the algorithm's accuracy, we synthesized two of the most promising polymer membranes predicted by this approach and found that they exceeded the upper bound for CO2/CH4 separation performance. This ML technique, which is trained using a relatively small body of experimental data (and no simulation data), evidently represents an innovative means of exploring the vast phase space available for polymer membrane design.
| S-EPMC7228755 | biostudies-literature
Project description:The extraction and subsequent separation of individual rare earth elements (REEs) from REE-bearing feedstocks represent a challenging yet essential task for the growth and sustainability of renewable energy technologies. As an important step toward overcoming the technical and environmental limitations of current REE processing methods, we demonstrate a biobased, all-aqueous REE extraction and separation scheme using the REE-selective lanmodulin protein. Lanmodulin was conjugated onto porous support materials using thiol-maleimide chemistry to enable tandem REE purification and separation under flow-through conditions. Immobilized lanmodulin maintains the attractive properties of the soluble protein, including remarkable REE selectivity, the ability to bind REEs at low pH, and high stability over numerous low-pH adsorption/desorption cycles. We further demonstrate the ability of immobilized lanmodulin to achieve high-purity separation of the clean-energy-critical REE pair Nd/Dy and to transform a low-grade leachate (0.043 mol % REEs) into separate heavy and light REE fractions (88 mol % purity of total REEs) in a single column run while using ∼90% of the column capacity. This ability to achieve, for the first time, tandem extraction and grouped separation of REEs from very complex aqueous feedstock solutions without requiring organic solvents establishes this lanmodulin-based approach as an important advance for sustainable hydrometallurgy.
| S-EPMC8614107 | biostudies-literature
Project description:Rare earth separation is still a major challenge in membrane science. Nitrogen-doped nanoporous graphene (NDNG) is a promising material for membrane separation, but it has not yet been tested for rare earth separation, and it is limited by multi-complex synthesis. Herein, we developed a one-step, facile, and scalable approach to synthesize NDNG with tunable pore size and controlled nitrogen content using confinement combustion. Nanoporous hydrotalcite from Zn(NO3)2 is formed between layers of graphene oxide (GO) absorbed with phenylalanine via confinement growth, thus preparing the sandwich hydrotalcite/phenylalanine/GO composites. Subsequently, area-confinement combustion of hydrotalcite nanopores is used to etch graphene nanopores, and the hydrotalcite interlayer as a closed flat nanoreactor induces two-dimensional space confinement doping of planar nitrogen into graphene. The membrane prepared by NDNG achieves separation of Sc3+ from the other rare earth ions with excellent selectivity (∼3.7) through selective electrostatic interactions of pyrrolic-N, and separation selectivity of ∼1.7 for Tm3+/Sm3+.
| S-EPMC7772569 | biostudies-literature
Project description:In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models. Our current study focuses on accurately predicting viscosity in liquid systems using MD descriptors. In this work, we curated a comprehensive dataset of over 4000 small organic molecules' viscosities from scientific literature, publications, and online databases. This dataset enabled us to develop quantitative structure-property relationships (QSPR) consisting of descriptor-based and graph neural network models to predict temperature-dependent viscosities for a wide range of viscosities. The QSPR models reveal that including MD descriptors improves the prediction of experimental viscosities, particularly at the small data set scale of fewer than a thousand data points. Furthermore, feature importance tools reveal that intermolecular interactions captured by MD descriptors are most important for viscosity predictions. Finally, the QSPR models can accurately capture the inverse relationship between viscosity and temperature for six battery-relevant solvents, some of which were not included in the original data set. Our research highlights the effectiveness of incorporating MD descriptors into QSPR models, which leads to improved accuracy for properties that are difficult to predict when using physics-based models alone or when limited data is available.
| S-EPMC10938832 | biostudies-literature
Project description:Machine learning is increasingly applied to Earth Observation (EO) data to obtain datasets that contribute towards international accords. However, these datasets contain inherent uncertainty that needs to be quantified reliably to avoid negative consequences. In response to the increased need to report uncertainty, we bring attention to the promise of conformal prediction within the domain of EO. Unlike previous uncertainty quantification methods, conformal prediction offers statistically valid prediction regions while concurrently supporting any machine learning model and data distribution. To support the need for conformal prediction, we reviewed EO datasets and found that only 22.5% of the datasets incorporated a degree of uncertainty information, with unreliable methods prevalent. Current open implementations require moving large amounts of EO data to the algorithms. We introduced Google Earth Engine native modules that bring conformal prediction to the data and compute, facilitating the integration of uncertainty quantification into existing traditional and deep learning modelling workflows. To demonstrate the versatility and scalability of these tools we apply them to valued EO applications spanning local to global extents, regression, and classification tasks. Subsequently, we discuss the opportunities arising from the use of conformal prediction in EO. We anticipate that accessible and easy-to-use tools, such as those provided here, will drive wider adoption of rigorous uncertainty quantification in EO, thereby enhancing the reliability of downstream uses such as operational monitoring and decision-making.
| S-EPMC11246475 | biostudies-literature