Separation Approach Research Articles

Recent trends in multidimensional countercurrent chromatography.

Countercurrent chromatography (CCC) is a potent separation approach known for its remarkable efficiency and capacity in preparation. It's applied as a substitute or combined with different chromatographic techniques, resulting in its rebranding as multidimensional CCC (MDCCC). Numerous essential mixtures from natural products contain hundreds or thousands of distinct components of importance. These mix types are too complicated to separate in any reasonable time using a single CCC dimension. However, if a multidimensional technique is utilized, where a complex mixture is separated by an initial dimension, smaller fractions of that separation are gathered. Each fraction is studied individually; complex mixes can be resolved relatively quickly. Thus, several MDCCC separation features have been studied to demonstrate their advantages, limitations, and prospective capacity to separate exceedingly complex mixtures. In this review, MDCCC aspects, including principles, multiple columns system, multilayer coil J-type, on-line monitoring system, and applications, have been thoroughly_explored.

Hybrid ANN-PSO modeling and risk assessment approach of molybdenum separation from tungsten in sulfate solutions by liquid–liquid extraction process with D2EHPA

The liquid–liquid extraction process for Mo(VI) in the presence of W(VI) from a sulfate solution with di-(2-ethylhexyl) phosphoric acid (D2EHPA) was studied using an artificial neural network (ANN) model coupled with a particle swarm optimization (PSO) algorithm. This study examined the influence of several operating parameters, including equilibrium pH, mixing time, reaction temperature, extractant concentration, and organic/aqueous (O/A) ratio, on the selective extraction of molybdenum from tungsten. In addition, the effects of different concentrations of ammonium bicarbonate on the metal stripping process from the loaded organic phase were analyzed. Subsequently, intelligent predictions regarding the molybdenum separation from tungsten were made using the ANN methodology in conjunction with the PSO technique. The numerical modeling results indicated that the coupled ANN-PSO approach demonstrated superior predictive performance, as evidenced by a higher correlation coefficient (R2) and lower absolute average relative error (AARE), in comparison to the simple ANN approach for predicting the selective extraction of molybdenum from tungsten. A Monte Carlo simulation (MCS) using a pseudo-random algorithm was employed to evaluate the uncertainty principle associated with the measurement-based input parameters that affect the separation factor response. The PERT distribution function was used to conduct the sensitivity analyses by incorporating mathematical formulae to generate sequences of random numbers. The findings of these analyses revealed that the factors with the greatest influence, arranged in descending order, were the equilibrium pH, D2EHPA concentration, O/A ratio, mixing time, and reaction temperature.

Biowaste-based porous carbon nanoparticle doped polymer dispersed ferroelectric liquid crystal composites: an impact on optical and electrical properties

Bio-waste-based porous carbon nanoparticles (PCNPs) were synthesized using green synthesis and investigated their doping effect on the optical and electrical properties of polymer-dispersed ferroelectric liquid crystals (PDFLCs) composites. Here we employed the polymerization-induced phase separation (PIPS) approach for constructing the PDFLCs. Our results indicate that the dispersion of PCNPs into the PDFLC material results in an alteration to several physical parameters, including morphology, dielectric permittivity, conductivity and optical band gap. A decrease in the ac conductivity of the doped samples was seen. Additionally, UV-Visible study reveals that inclusion of PCNPs resulted in a decrease in the optical band gap of PDFLC, with a value of approximately 3.1 eV. These findings demonstrate the potential of using PCNPs as dopants in PDFLCs for various applications, including sensing, energy storage and optoelectronics.

Amplitude-preserving P-/S-wavefield separation with the discontinuous Galerkin method on unstructured meshes

The decoupling of P and S waves is an essential prerequisite for elastic reverse time migration, which effectively reduces crosstalk artifacts, but most wavefield separation algorithms are implemented on uniform rectangular grids. We develop an amplitude- and phase-preserving P- and S-wavefield separation approach on unstructured meshes, which can effectively decompose the original elastic wavefield into P and S wavefields. The isotropic case is considered. With the aid of viscoelastic theory, we choose to attenuate P or S waves and preserve the other wave mode, so as to achieve wavefield decomposition. Viscoelastic wave equations are first reformulated as decoupling wave equations with a selective strong attenuation. We then use the discontinuous Galerkin (DG) method to simulate decoupling P or S wavefield propagation on triangular and tetrahedral meshes. We adopt a quadrature-free DG approach and the arbitrary mesh is mapped into the reference mesh for numerical calculation, where no additional volume and surface integrations are involved. The amplitude and phase information of this vector decomposition agrees with that of the original elastic data. Four numerical examples are used to demonstrate the superior performance of this vector decomposition algorithm. The isotropic example indicates the applicability and correctness of our scheme and the second example displays superiority in handling strong velocity contrasts. The third example exhibits mesh flexibility in dealing with complex structures, such as caves, faults, and undulating surfaces. The last example indicates the effectiveness of our developed algorithm extended to a 3D case.

Microfluidics-assisted Tumor Cell Separation Approaches for Clinical Applications: An Overview on Emerging Devices.

Recent advances in science and technology have led to revolutions in many different scientific and industrial fields. The term lab on a chip, or in other word, performing a variety of complex analyzes in just a short time and in a very small space, is a term that has become very common in recent years, and what used to be a dream has now come to life in practice. In this paper, we has been tried to investigate a specific type of lab technology on a chip, which is of course one of the most common, namely the knowledge and technology of cell separation by using microfluidic technique that can be separated based on size and deformation, adhesion and electrical properties. The tissue of the human body is degraded due to injury or aging, which is often tried to treat this tissue disorder by using drugs, but they are not always enough. Stem cell-based medicine is a novel form of medicine which promises the restoration or regeneration of tissues and functioning organs. Although many models of microfluidic systems have been designed for cell separation, the choice of appropriate device to achieve a reliable result is presented as a challenge. So, in this study, Fluorescence Activated Cell Sorting (FACS), Dielectrophoresis (DEP), Magnetic Activated Cell Sorting (MACS) and Acoustic microfluidic system are four distinct categories of active microfluidic systems explored. Also, the advantages, disadvantages and the current status of the mentioned devices in these methods, are reviewed.

An Ultra-High-Performance Liquid Chromatography Coupled with Tandem Mass Spectrometry Method with Online Solid-Phase Extraction Sample Preparation for the High-Throughput and Sensitive Determination of Ostarine in Human Urine.

Ostarine is frequently misused as a selective androgen receptor modulator (SARM) in sports. Consequently, there is a pressing need for reliable and simple approaches to monitor its presence in biological systems. In this work, we developed a two-dimensional analytical method utilizing online solid-phase extraction (online-SPE) in conjunction with ultra-high-performance liquid chromatography and tandem mass spectrometry (triple quadrupole). This automated 2D separation approach is characterized by minimum manual steps in the sample preparation (only dilute-and-shoot), reflecting high sample throughput and the reliability of analytical data. It provides favorable performance parameters, including a limit of detection of 0.5 pg/mL, high accuracy (relative error = 1.6-7.5%), precision (relative standard deviation = 0.8-4.5%), and sensitivity. Additionally, it demonstrates excellent linearity (r2 = 0.9999) in the calibration range of 0.05 to 25 ng/mL and robustness, with no carryover effects observed. This comparative study revealed a two-decadic-order-lower LOD of the SPE-UHPLC-MS/MS method to the corresponding UHPLC-MS/MS method and the lowest one in the group of currently published LC-MS methods. The World Anti-Doping Agency screening and confirmation criteria were met through the analysis of spiked urine samples from ten healthy volunteers. Accordingly, the proposed method is suitable for routine use in antidoping laboratories.

Real-Time On-the-Fly Motion Planning for Urban Air Mobility via Updating Tree Data of Sampling-Based Algorithms Using Neural Network Inference

In this study, we consider the problem of motion planning for urban air mobility applications to generate a minimal snap trajectory and trajectory that cost minimal time to reach a goal location in the presence of dynamic geo-fences and uncertainties in the urban airspace. We have developed two separate approaches for this problem because designing an algorithm individually for each objective yields better performance. The first approach that we propose is a decoupled method that includes designing a policy network based on a recurrent neural network for a reinforcement learning algorithm, and then combining an online trajectory generation algorithm to obtain the minimal snap trajectory for the vehicle. Additionally, in the second approach, we propose a coupled method using a generative adversarial imitation learning algorithm for training a recurrent-neural-network-based policy network and generating the time-optimized trajectory. The simulation results show that our approaches have a short computation time when compared to other algorithms with similar performance while guaranteeing sufficient exploration of the environment. In urban air mobility operations, our approaches are able to provide real-time on-the-fly motion re-planning for vehicles, and the re-planned trajectories maintain continuity for the executed trajectory. To the best of our knowledge, we propose one of the first approaches enabling one to perform an on-the-fly update of the final landing position and to optimize the path and trajectory in real-time while keeping explorations in the environment.

Kinetics and mechanism of selective leaching of bismuth from molybdenite and bismuthinite mixed ore

In the conventional beneficiation process for handling mixed ore containing molybdenite and bismuthinite, the co-extraction issues involving molybdenum and bismuth, exemplified by mutual content of molybdenite and bismuthinite, may lead to the shutdown of the production line. Under acidic conditions, Bi3+ and Cl− can form stable coordination complex anions, whereas Mo4+, Mo6+, and Cl− do not exhibit this behavior. This insight offers a novel approach for the chemical separation of molybdenum and bismuth from mixed ore using hydrochloric acid. Through a kinetic study of the hydrochloric acid leaching process applied to bismuth extraction from mixed molybdenite and bismuthinite ore, we have investigated various factors that influence leaching efficiency, such as stirring speed, particle size, acid concentration, and reaction temperature. Experimental results and computational results have revealed that the apparent activation energy is 56 kJ mol−1. The kinetic analysis suggests that the leaching process is governed by the product layer diffusion model. Moreover, the use of hydrochloric acid in leaching the mixed ore is shown to alter the surface structure, thereby increasing the effective reaction area. This, in turn, leads to improved leaching efficiency and enhances the overall leaching process.

An optimized deep learning based depthwise separable MobileNetV3 approach for automatic finger vein recognition system

An optimized deep learning based depthwise separable MobileNetV3 approach for automatic finger vein recognition system

Recent Advances in Metalloproteomics.

Interactions between proteins and metal ions and their complexes are important in many areas of the life sciences, including physiology, medicine, and toxicology. Despite the involvement of essential elements in all major processes necessary for sustaining life, metalloproteomes remain ill-defined. This is not only owing to the complexity of metalloproteomes, but also to the non-covalent character of the complexes that most essential metals form, which complicates analysis. Similar issues may also be encountered for some toxic metals. The review discusses recently developed approaches and current challenges for the study of interactions involving entire (sub-)proteomes with such labile metal ions. In the second part, transition metals from the fourth and fifth periods are examined, most of which are xenobiotic and also tend to form more stable and/or inert complexes. A large research area in this respect concerns metallodrug-protein interactions. Particular attention is paid to separation approaches, as these need to be adapted to the reactivity of the metal under consideration.

Ultra-selective uranium separation by in-situ formation of π-f conjugated 2D uranium-organic framework

With the rapid development of nuclear energy, problems with uranium supply chain and nuclear waste accumulation have motivated researchers to improve uranium separation methods. Here we show a paradigm for such goal based on the in-situ formation of π-f conjugated two-dimensional uranium-organic framework. After screening five π-conjugated organic ligands, we find that 1,3,5-triformylphloroglucinol would be the best one to construct uranium-organic framework, thus resulting in 100% uranium removal from both high and low concentration with the residual concentration far below the WHO drinking water standard (15 ppb), and 97% uranium capture from natural seawater (3.3 ppb) with a record uptake efficiency of 0.64 mg·g−1·d−1. We also find that 1,3,5-triformylphloroglucinol can overcome the ion-interference issue such as the presence of massive interference ions or a 21-ions mixed solution. Our finds confirm the superiority of our separation approach over established ones, and will provide a fundamental molecule design for separation upon metal-organic framework chemistry.

Unveiling the potential of novel recyclable deep eutectic solvent pretreatment: Effective separation of lignin from poplar hydrolyzed residue

To effectively convert the fermentable sugars present in lignocellulosic biomass into biofuels and additional value-added products, it is crucial to remove lignin from the biomass. With the intention of expeditiously remove lignin from poplar wood and improve cellulose saccharification, an innovative ternary deep eutectic solvent (DES) benzyl triethyl ammonium chloride-ethylene glycol-FeCl3 (T-EG-F) was studied for the pretreatment of poplar hydrolyzed residue (PHR). The results revealed that following T-EG-F DES pretreatment at 130 °C for 4 h, the lignin removal rate reached 91.88 %. The effect of DES on PHR and regenerated lignin was comprehensively investigated using X-ray diffractometer (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), Thermogravimetric (TG) and other characterization methods, providing valuable insights into the mechanism of this innovative biomass pretreatment. Moreover, there was a significant improvement in the enzyme digestibility of the DES pretreatment residue. At 48 h, the enzyme load of 30 FPU/g cellulose achieved a remarkable enzyme digestibility of 97.31 %, and this value exhibited a notable increase of 6.56 times compared to the untreated poplar sample. In addition, the T-EG-F could be recycled and reused. This study demonstrates that the potential of T-EG-F DES pretreatment as a green and efficient method for lignin dissociation from lignocellulosic biomass, offering a promising approach for biomass component separation.

Benchmarking the accuracy of the separable resolution of the identity approach for correlated methods in the numeric atom-centered orbitals framework.

Four-center two-electron Coulomb integrals routinely appear in electronic structure algorithms. The resolution-of-the-identity (RI) is a popular technique to reduce the computational cost for the numerical evaluation of these integrals in localized basis-sets codes. Recently, Duchemin and Blase proposed a separable RI scheme [J. Chem. Phys. 150, 174120 (2019)], which preserves the accuracy of the standard global RI method with the Coulomb metric and permits the formulation of cubic-scaling random phase approximation (RPA) and GW approaches. Here, we present the implementation of a separable RI scheme within an all-electron numeric atom-centered orbital framework. We present comprehensive benchmark results using the Thiel and the GW100 test set. Our benchmarks include atomization energies from Hartree-Fock, second-order Møller-Plesset (MP2), coupled-cluster singles and doubles, RPA, and renormalized second-order perturbation theory, as well as quasiparticle energies from GW. We found that the separable RI approach reproduces RI-free HF calculations within 9meV and MP2 calculations within 1meV. We have confirmed that the separable RI error is independent of the system size by including disordered carbon clusters up to 116 atoms in our benchmarks.

Multimodal few-shot classification without attribute embedding

Multimodal few-shot learning aims to exploit complementary information inherent in multiple modalities for vision tasks in low data scenarios. Most of the current research focuses on a suitable embedding space for the various modalities. While solutions based on embedding provide state-of-the-art results, they reduce the interpretability of the model. Separate visualization approaches enable the models to become more transparent. In this paper, a multimodal few-shot learning framework that is inherently interpretable is presented. This is achieved by using the textual modality in the form of attributes without embedding them. This enables the model to directly explain which attributes caused it to classify an image into a particular class. The model consists of a variational autoencoder to learn the visual latent representation, which is combined with a semantic latent representation that is learnt from a normal autoencoder, which calculates a semantic loss between the latent representation and a binary attribute vector. A decoder reconstructs the original image from concatenated latent vectors. The proposed model outperforms other multimodal methods when all test classes are used, e.g., 50 classes in a 50-way 1-shot setting, and is comparable for lesser number of ways. Since raw text attributes are used, the datasets for evaluation are CUB, SUN and AWA2. The effectiveness of interpretability provided by the model is evaluated by analyzing how well it has learnt to identify the attributes.

Coupling Electrochemical Sulfion Oxidation with CO2 Reduction over Highly Dispersed p‐Bi Nanosheets and CO2‐Assisted Sulfur Extraction

AbstractWe report herein an electrocatalytic CO2 reduction‐coupled sulfion oxidation system for the co‐productions of valuable formate and sulfur at much enhanced atom utilization. Specifically, an organic ligand‐assisted two‐step reconstruction approach has been developed to fabricate the highly dispersed p‐Bi nanosheets (p‐Bi NSs) for cathodic CO2 reduction reaction (CO2RR), and meanwhile porous Co−S nanosheets (Co−S NSs) was applied for anodic sulfion oxidation reaction (SOR). Significantly high Faradaic Efficiencies of about 90 % for formate production by CO2RR in a wide potential range from −0.6 V to −1.1 V, and excellent SOR performances including an ultra‐low onset potential of about 0.2 V and recycle capacity of S2− in the 0.1 M and 0.5 M S2− solutions, have been simultaneously achieved. In the meantime, both the structure transformation of the catalysts and the reaction pathways are explored and discussed in detail. A two‐electrode CO2RR||SOR electrolyzer equipped with above electrocatalysts has been established, which features as low as about 1.5 V to run the electrolyzer at 100 mA cm−2, manifesting extremely lowered electricity consumption in comparison to conventional CO2RR system. Moreover, a sulfur separation approach has been proposed by using CO2, which is efficient, environmentally friendly and cost effective with value‐added NaHCO3 be obtained as the byproduct.

Coupling Electrochemical Sulfion Oxidation with CO2 Reduction over Highly Dispersed p-Bi Nanosheets and CO2 -Assisted Sulfur Extraction.

We report herein an electrocatalytic CO2 reduction-coupled sulfion oxidation system for the co-productions of valuable formate and sulfur at much enhanced atom utilization. Specifically, an organic ligand-assisted two-step reconstruction approach has been developed to fabricate the highly dispersed p-Bi nanosheets (p-Bi NSs) for cathodic CO2 reduction reaction (CO2 RR), and meanwhile porous Co-S nanosheets (Co-S NSs) was applied for anodic sulfion oxidation reaction (SOR). Significantly high Faradaic Efficiencies of about 90 % for formate production by CO2 RR in a wide potential range from -0.6 V to -1.1 V, and excellent SOR performances including an ultra-low onset potential of about 0.2 V and recycle capacity of S2- in the 0.1 M and 0.5 M S2- solutions, have been simultaneously achieved. In the meantime, both the structure transformation of the catalysts and the reaction pathways are explored and discussed in detail. A two-electrode CO2 RR||SOR electrolyzer equipped with above electrocatalysts has been established, which features as low as about 1.5 V to run the electrolyzer at 100 mA cm-2 , manifesting extremely lowered electricity consumption in comparison to conventional CO2 RR system. Moreover, a sulfur separation approach has been proposed by using CO2 , which is efficient, environmentally friendly and cost effective with value-added NaHCO3 be obtained as the byproduct.

Little evidence for a noun bias in Tseltal spontaneous speech

This study examines whether children acquiring Tseltal (Mayan) demonstrate a noun bias – an overrepresentation of nouns in their early vocabularies. Nouns, specifically concrete and animate nouns, are argued to universally predominate in children’s early vocabularies because their referents are naturally available as bounded concepts to which linguistic labels can be mapped. This early advantage for noun learning has been documented using multiple methods and across a diverse collection of language populations. However, past evidence bearing on a noun bias in Tseltal learners has been mixed. Tseltal grammatical features and child–caregiver interactional patterns dampen the salience of nouns and heighten the salience of verbs, leading to the prediction of a diminished noun bias and perhaps even an early predominance of verbs. We here analyze the use of noun and verb stems in children’s spontaneous speech from egocentric daylong recordings of 29 Tseltal learners between 0;9 and 4;4. We find weak to no evidence for a noun bias using two separate analytical approaches on the same data; one analysis yields a preliminary suggestion of a flipped outcome (i.e. a verb bias). We discuss the implications of these findings for broader theories of learning bias in early lexical development.

Some Novel Fusion and Fission Phenomena for an Extended (2+1)-Dimensional Shallow Water Wave Equation

An extended (2+1)-dimensional shallow water wave (SWW) model which can describe the evolution of nonlinear shallow water wave propagation in two spatial and temporal coordinates, is systematically studied. The multi-linear variable separation approach is addressed to the extended (2+1)-dimensional SWW equation. The variable separation solution consisting of two arbitrary functions is obtained, by assumption, from a specific ansatz. By selecting these two arbitrary functions as the exponential and trigonometric forms, resonant dromion, lump, and solitoff solutions are derived. Meanwhile, some novel fission and fusion phenomena including the semifoldons, peakons, lump, dromions, and periodic waves are studied with graphical and analytical methods. The results can be used to enhance the variety of the dynamics of the nonlinear wave fields related by engineering and mathematical physics.

The embodied carbon of paracetamol and the consumables associated with different routes of administration in pediatrics

BackgroundThe aim of this study was to estimate the embodied carbon of different formulations of paracetamol, its packaging and the consumables used in administration within pediatrics. MethodsA set dose of 750 mg was chosen to represent complex dosing seen in pediatrics and then two separate approaches to measuring the embodied carbon were taken. A top-down environmentally extended input-output analysis for the drug and a bottom-up approach using the emissions factors for the primary material was used for the packaging and consumables. ResultsAll oral methods of administration have the lowest embodied carbon when accounting for the drugs, consumables, and packaging. ConclusionThis study demonstrates how working across disciplines we can look for ways in which we can minimize the carbon cost of care. This study finds that when accounting for patient safety, acceptability within pediatrics and the embodied carbon, all non-IV methods are preferable.

A PRECISION HEALTH APPROACH FOR OSTEOARTHRITIS: PREDICTION OF RAPID KNEE OSTEOARTHRITIS PROGRESSION USING AUTOMATED MACHINE LEARNING

Precision health aims to develop personalised and proactive strategies for predicting, preventing, and treating complex diseases such as osteoarthritis (OA). Due to OA heterogeneity, which makes developing effective treatments challenging, identifying patients at risk for accelerated disease progression is essential for efficient clinical trial design and new treatment target discovery and development.To create a reliable and interpretable precision health tool that predicts rapid knee OA progression over a 2-year period from baseline patient characteristics using an advanced automated machine learning (autoML) framework, “Autoprognosis 2.0”.All available 2-year follow-up periods of 600 patients from the FNIH OA Biomarker Consortium were analysed using “Autoprognosis 2.0” in two separate approaches, with distinct definitions of clinical outcomes: multi-class predictions (categorising disease progression into pain and/or radiographic progression) and binary predictions. Models were developed using a training set of 1352 instances and all available variables (including clinical, X-ray, MRI, and biochemical features), and validated through both stratified 10-fold cross-validation and hold-out validation on a testing set of 339 instances. Model performance was assessed using multiple evaluation metrics. Interpretability analyses were carried out to identify important predictors of progression.Our final models yielded higher accuracy scores for multi-class predictions (AUC-ROC: 0.858, 95% CI: 0.856-0.860) compared to binary predictions (AUC-ROC: 0.717, 95% CI: 0.712-0.722). Important predictors of rapid disease progression included WOMAC scores and MRI features. Additionally, accurate ML models were developed for predicting OA progression in a subgroup of patients aged 65 or younger.This study presents a reliable and interpretable precision health tool for predicting rapid knee OA progression. Our models provide accurate predictions and, importantly, allow specific predictors of rapid disease progression to be identified. Furthermore, the transparency and explainability of our methods may facilitate their acceptance by clinicians and patients, enabling effective translation to clinical practice.