Extraction Step Research Articles

Evaluation of fish meal freshness using a metal-oxide semiconductor electronic nose combined with the long short-term memory feature extraction method.

To improve the classification and regression performance of the total volatile basic nitrogen (TVB-N) and acid value (AV) of different freshness fish meal samples detected by a metal-oxide semiconductor electronic nose (MOS e-nose), 402 original features, 62 manually extracted features, manually extracted and selected features by the RFRFE method, and the features extracted by the long short-term memory (LSTM) network were used as inputs to identify the freshness. The classification performance of the freshness grades and the estimation performance of the TVB-N and AV values of fish meal with different freshness were compared. According to the sensor response curve, preprocessing and feature extraction steps were first applied to the original data. Then, five classification algorithms and four regression algorithms were used for modeling. The results showed that a total of 30 features were extracted using the LSTM network, and the number of extracted features was significantly reduced. In the classification, the highest accuracy rate of 95.4% was obtained using the support vector machine method. In the regression, the least squares support vector regression method obtained the best root mean square error (RMSE). The coefficient of determination (R2), RMSE, and relative standard deviation (RSD) between the predicted value of TVBN and the actual value were 0.963, 11.01, and 7.9%, respectively. The R2, RMSE, and RSD between the predicted value of AV and the actual value were 0.972, 0.170, and 6.05%, respectively. The LSTM feature extraction method provided a new method and reference for feature extraction using an E-nose to identify other animal-derived material samples.

Determination of capsaicin at trace levels in different food, biological and environmental samples by quadruple isotope dilution-gas chromatography mass spectrometry after its preconcentration

Despite the therapeutic properties of capsaicin for some diseases, it shows some side effects for human health. The goal of this study was to develop a precise and accurate analytical strategy for the trace determination of capsaicin in different food, biological and environmental samples including pepper, saliva and wastewater by gas chromatography–mass spectrometry (GC–MS) after spraying-based fine droplet formation-liquid phase microextraction (SFDF-LPME) and quadruple isotope dilution (ID4) method. Acetic anhydride was used as derivatizing agent, and the extraction method was used to enrich the analyte derivative to reach low detection limits. Under the optimum conditions, limit of detection (LOD) and limit of quantitation (LOQ) were determined to be 0.33 and 1.10 µg/kg, respectively. Percent recoveries calculated for SFDF-LPME-GC-MS method ranged between 84.1 and 131.7 %. After the application of ID4-SFDF-LPME-GC-MS method, percent recoveries were obtained in the range of 94.9 and 104.0 % (%RSD ≤ 2.8) for the selected samples. It is obvious that the isotope dilution-based method provided high accurate and precise results due to the elimination of errors during the derivatization, extraction and measurement steps.

Dithizone functionalized carbon nanofibers as novel nanomaterial for microextraction and determination of mercury in non-alcoholic beverages

In this work, a new nanomaterial obtained by surface functionalization of carbon nanofibers with dithizone (CNF@DTZ) was implemented as adsorbent in the dispersive micro-solid phase extraction technique. Preconcentration and determination of Hg in non-alcoholic beverage samples including fruit juices and drinking water were performed with the proposed method. Important parameters of the extraction and back-extraction steps were optimized. Using 3 mg CNF@DTZ only, an extraction efficiency of 98.0 % was achieved for 30 mL of sample. Quantitative back-extraction was achieved with 600 µL of 12 mol/L HCl. A sensitivity enhancement factor of 50 was obtained. The calibration curve was linear in the range from 0.23 to 4.0 µg/L, the LOD was 0.07 µg/L and the RSD% was 3.5 % (2 µg/L Hg, n = 10). This methodology was successfully applied on spiked samples with recoveries from 96.0 to 100 %. This work reports the first application of CNF@DTZ in the dispersive micro-solid phase extraction technique for trace Hg determination in non-alcoholic beverages.

Sequential Fractionation of Heavy Metals from Flooded and Non-Flooded Soils in Obunagha Community, Bayelsa State, Nigeria

Soil samples from flooded and non-flooded areas of different farmlands in Obunagha community, Bayelsa State were collected for this study. The aim of the study was to investigate the chemical fractionation and bioavailability of Cr, Cd, Zn, Ni, & Fe in flooded and non-flooded farmlands. The soil samples were fractionated into six chemical fractions, each containing different heavy metal concentrations and analyzed using flame atomic absorption spectrophotometer. The highest average concentration for Cadmium (Cd) is 1.50g/kg, Zinc (Zn) is 7.53g/kg, Nickel (Ni) is 10.54g/kg, Iron (Fe) is 9.52g/kg, and Chromium (Cr) is 12.62g/kg all from the flooded areas of the farmlands. Sequential extraction showed heavy metals distributed throughout all extraction steps, with the water-soluble fraction prevailing, potentially increasing the mobility and bioavailability of these metals. The %bioavailability ranged from 37.98% – 90.00%. The results indicated also that the average concentration of the metals in flood affected farmland were more than the non-flooded areas. The results indicated that floods affect farmland metal content by altering contaminant bioavailability, causing adverse environmental effects. Also, the results of the heavy metals analyses revealed that the levels of nickel, zinc, chromium, cadmium and iron in all the farmland soil samples are above the maximum allowable concentration for heavy metals in soil set by WHO Guidelines provided. Hence, the farmland soils are not safe for agricultural activities. We therefore, recommend regular monitoring of the soils to prevent excessive build-up of these toxic heavy metals.

Modulation of physico-chemical and technofunctional properties of quinoa protein isolate: Effect of precipitation acid

The typically low solubility and gelation capacity of plant proteins can impose challenges in the design of high-quality plant-based foods. The acid used during the precipitation step of plant protein isolate extraction can influence protein functionality. Here, acetic acid and citric acid were used to extract quinoa protein isolate (QPI) from quinoa flour, as these acids are more kosmotropic than the commonly used HCl, promoting the stabilisation of the native protein structure. While proximate analysis showed that total protein was similar for the three isolates, precipitation with kosmotropic acids increased soluble protein, which correlated positively with gel strength. Microstructure analysis revealed that these gels contained a less porous protein network with lipid droplet inclusions. This study shows that the choice of precipitation acid offers an opportunity to tailor the properties of quinoa protein isolate for application, a strategy that is likely applicable to other plant protein isolates.

Rapid and Portable Presumptive Loop‐Mediated Isothermal Amplification Assays for the Detection of the Invasive Corn Snake (Pantherophis guttatus)

ABSTRACTThe exotic pet trade is a major pathway for the introduction, establishment, and spread of novel invasive alien species. Reptiles are common in the exotic pet trade and are prominent invasive alien vertebrate species that have dire impacts if allowed to establish. The North American corn snake (Pantherophis guttatus) is particularly common in the international pet trade and has been identified as a vertebrate pest priority species in Australia due to widespread climate suitability and prevalence in pre‐ and post‐border seizure records. Consequently, rapid, and presumptive post‐border biosecurity detection is essential to prevent its establishment and spread. Loop‐mediated isothermal amplification (LAMP) is an emerging biosecurity tool that has shown promise for rapid detection of several high‐risk species. We developed two LAMP assays for the detection of P. guttatus, validated against: synthetic DNA; DNA extracted from snap‐frozen tissue, and shed skins; and then compared their performance for the detection of trace DNA collected from swabs of glass tanks post reptile presence. Our results include laboratory optimization and assessment of two mobile devices for in‐field integration (Franklin Real‐Time PCR Thermocycler, Biomeme, USA, and Genie III, Optigene, UK). The results indicate that LAMP is a viable biosecurity tool, with DNA detection possible for a range of sample types in a total of c.30 min, when including a rapid extraction step (8 min). Herein, we provide tools for rapid, presumptive detection of the North American corn snake from trace DNA samples in Australian biosecurity and wildlife compliance settings.

Alternative protocol leading to rapid identification of Actinomycetes isolated from Algerian desertic soil by MALDI-TOF mass spectrometry

Matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) is the first-line method for the rapid identification of most cultured microorganisms. As for Streptomyces strains, MALDI-TOF MS identification is complicated by the characteristic incrustation of colonies in agar and the strong cell wall of Actinomycetes cells requiring the use of alternative protein extraction protocols. In this study, we developed a specific protocol to overcome these difficulties for the MALDI-TOF MS identification of Actinomycetes made on solid medium. This protocol includes incubation of colony removed from agar plate with the beta-agarase enzyme, followed by a mechanical lysis and two washes by phosphate buffer and ethanol. Twenty-four Streptomyces and two Lentzea strains isolated from Algerian desertic soils were first identified by 16S rRNA sequencing as gold standard method, rpoB gene was used as a secondary gene target when 16S rRNA did not allow species identification. In parallel the isolates were identified by using the MALDI-TOF MS protocol as reported. After the expansion of the database with the inclusion of this MSPS, the strains were analyzed again in MALDI Biotyper, and all were identified. This work demonstrates that the rapid identification of Actinomycetes can be obtained without protein extraction step frequently used in MALDI-TOF mass spectrometry with this type of microorganisms.

Direct hydrolysis of einkorn whole grain flour proteins for the generation of bioactive peptides using various proteases

In this study, it was aimed to investigate the direct release of BAPs from einkorn flour in one-step process. Thus, the protein extraction step was eliminated, thereby reducing processing cost. Commercial proteases (Alcalase, Flavourzyme, Neutrase, and Trypsin), and crude enzyme from Bacillus mojavensis sp. EBTA7 were used for hydrolyzing einkorn flour (30 %, w/v) solutions at 50–60 °C. The supernatants after centrifugation were used for bioactivity and techno-functionality tests. All hydrolysates demonstrated significant antioxidant capacities, with values ranging from 17.7 to 33.0 μmol TE/g for DPPH, 107 to 190 μmol TE/g for ABTS, and 0.09 to 3.08 mg EDTA/g for ion-chelating activities. Alcalase and Flavourzyme hydrolysis had the highest DPPH activities, while Bacillus mojavensis sp. EBTA7 enzyme yielded relatively high ABTS and ion-chelating activities. Notably, Bacillus mojavensis sp. EBTA7 crude enzyme hydrolysates demonstrated higher oil absorption capacity (2.94 g oil/g hydrolysate), robust emulsion (227 min), and foam stability (94 %) compared to commercial enzymes. FTIR spectroscopy confirmed variations in the secondary structure of peptides. All hydrolysates exhibited negative zeta potentials. The SDS-PAGE showcased MW ranged from 14 to 70 kDa, which was influenced by both the enzyme type and the degree of hydrolysis. Overall, Bacillus mojavensis sp. EBTA7 hydrolysates revealed considerable bio and techno-functional characteristics.

Motor imagery electroencephalography channel selection based on deep learning: A shallow convolutional neural network

Electroencephalography (EEG) motor imagery (MI) signals have recently attracted much attention because of their potential to communicate with the surrounding environment in a specific way without the need for muscular and physical movement. Despite these advantages, these signals are difficult to detect due to their low signal to noise (SNR) rate and non-stationary and dynamic nature. Convolutional neural networks (CNN) can extract appropriate spatial and temporal features without the need for separate feature extraction and classification steps. Clean input data for CNN significantly improves its performance, but sometimes, common interference occurs between multi-channel signals. This challenge, along with the noisy EEG signals, degrades the performance of CNN networks. This paper presents a channel selection method based on convolutional neural networks to obviate these challenges. The proposed shallow convolutional neural network (SCNN) is designed with temporal convolution and pointwise convolution (using 1×1 convolution) to select the best channel with minimal computational load. Following the channel selection step, the multi-layer fusion CNN model was used to classify the signals. Choosing suitable and relevant EEG channels for motor imagery tasks makes it possible to create appropriate inputs for the classification model so that the model can be improved in the end. For High Gamma Dataset and Brian-Computer Interface (BCI) Competition IV-2a, the following accuracies were obtained: 81.15% and 72.01%, which are higher than when the other channel selections such as Mutual Information, Sequential Feature Forward Selection (SFFS) and wrapper methods are used.

Contribution of sequential pretreatments and enzyme-assisted extraction to rice bran protein yield improvement

Alcalase is commonly used in enzyme-assisted extraction of rice bran protein. However, broad handling of rice bran can affect proteins before proteolysis. Various pretreatments, including thermal stabilization, washing, defatting, alkaline extraction, and enzymatic cellulosic degradation, and combinations were identified regarding the present and possible handling processes of rice bran protein extraction to investigate their effects on the proteolysis of the protein afterward. Then, the protein extraction yield and proteolytic abilities were compared. The thermal stabilization of the rice bran strongly affects enzymatic protein extraction, which more elaborate extraction protocols can mitigate. Rice bran oil can be extracted earlier without seriously affecting the protein extraction. Washing removes native protease inhibitors and hence speeds up the protein extraction. Alkaline extraction is highly efficient but can cause protein denaturation, generating a challenging protein separation from the gel-like structure. The protein extraction with enzymatic (Ultimase) pretreatment is an efficient method that allows high hydrolysis of intact rice bran protein during the protein extraction step.

Metabolite analysis of 14C-labeled chloromethylisothiazolinone/methylisothiazolinone for toxicological consideration of inhaled isothiazolinone biocides in lungs

5-Chloro-2-methyl-4-isothiazolin-3-one (CMIT) and 2-methyl-4-isothiazolin-3-one (MIT) used as preservatives in various products, including humidifier disinfectants, presents substantial health hazards. This research delves into the toxicological assessments of CMIT/MIT in the respiratory system using animal models. Through the synthesis of radiolabeled [14C]CMIT and [14C]MIT, we investigated the biological uptake and in vivo behaviors of CMIT/MIT in the respiratory tissues following intratracheal exposure. Quantitative whole-body autoradiography (QWBA) revealed significant persistence of CMIT/MIT in lung tissue. In addition, radio high-performance liquid chromatography (radio-HPLC) with tandem mass spectrometry (LC-MS/MS) was employed for metabolite profiling and identification. Notably, around 28% of the radiolabel was retained in tissue after the extraction step, suggesting covalent binding of CMIT/MIT and their metabolites with pulmonary biomolecules. This observation demonstrates the propensity of the electrophilic isothiazolinone ring in CMIT/MIT to undergo chemical interactions with biothiols in proteins and enzymes, fostering irreversible alterations of biomolecules. Such accumulations of transformations could result in direct toxicity at both cellular and organ levels. Additionally, the detection of metabolites, including a MIT dimer conjugated with glutathione (GSH), as analyzed by mass spectrometry indicates the possible reduction of cellular GSH levels and subsequent oxidative stress. This investigation offers an in-depth insight into the toxic mechanisms of CMIT/MIT, underlying their capability to engage in complex formations with biomacromolecules and induce pronounced respiratory toxicity. These results highlight the imperative for stringent safety assessments of these chemicals, advocating for improved public health and safety measures in the use of chemicals.

Multi-Relation Extraction for Cybersecurity Based on Ontology Rule-Enhanced Prompt Learning

In the domain of cybersecurity, available annotated data are often scarce, especially for Chinese cybersecurity datasets, often necessitating the manual construction of datasets. The scarcity of samples is one of the challenges in researching cybersecurity, especially for the “no-relation” class. Since the annotation process typically focuses only on known relation classes, there are usually no training samples for the “no-relation” class. This poses a zero-shot classification problem, where during the classification process, there is a tendency to classify into a class with a relationship. Zero-shot classification tasks are particularly challenging in this context. Moreover, most relation classification models currently need to traverse all relations to calculate the class with the highest probability. Therefore, the problem of “computational redundancy” is another challenge faced. Thus, how to accurately and efficiently acquire cyberspace knowledge from heterogeneous data sources and address the challenges such as sample scarcity, zero-shot recognition, and computational redundancy is the main focus of this chapter. To address these problems, this chapter designs a multi-relation extraction model based on ontology rule-enhanced prompt learning, which is a parameter-sharing-based multi-task model. By introducing prompt learning, which has shown significant effectiveness in the few-shot domain, this chapter designs prompt templates combining discrete and continuous tokens and uses rule injection in prompt learning to solve the difficulties in zero-shot recognition of “no-relation” and computational redundancy issues, achieving efficient and accurate multi-relation extraction. Specifically, by constructing sub-prompts to achieve an efficient combination of templates, a parameter-sharing structure is used to implement knowledge extraction step by step: The first step constructs entity prompt templates combining discrete and continuous tokens, identifying the classes of two entities based on prompt learning. The second step involves rule injection, identifying whether it belongs to the “no-relation” class based on the combination of sub-prompts; if there is no connection between the classes of two entities, it is classified as “no relation”; if a connection exists, the candidate relation set is filtered out. The third step uses the pre-trained model and vectors from the first step, utilizing prompt learning and rule judgment to determine the relation class from the candidate relation set. Finally, the effectiveness of our model is validated on the general datasets TACRED, ReTACRED, and the cybersecurity dataset constructed in this paper.

Closed-loop resynthesis of LiNiCoAlO2 cathode active materials from the industrial leachate of spent li-ion batteries

The burgeoning electric vehicle market is significantly leading to a substantial increase in spent lithium-ion batteries (LIBs), emphasizing the need for efficient recycling to address raw material supply challenges. The resynthesis of cathode active materials, one of emerging efficient LIB recycling methods, refers to the synthesis of precursors from the leachate of spent LIBs without the separation of each element. While the resynthesis of LiNixCoyMnzO2 (NCM) from the Mn-rich industrial leachate of spent LIBs becomes popular nowadays, there is no practical approach for the resynthesis of LiNixCoyAlzO2 (NCA) from the industrial leachate. For the resynthesis of NCA, an additional purification step of solvent extraction with di(2-ethylhexyl) phosphoric acid for Mn removal is necessary. NCA is resynthesized from the Mn-removed leachate, after adjusting the Ni:Co ratio of precursors and sintering with Li and Al sources. While the initial discharge capacity of resynthesized NCA is slightly reduced to the electrochemically inactive impurities of Mg and Mn, its rate capability and cyclability are improved compared to pristine NCA from virgin materials. The effects of major impurities of Mg and Mn on the crystal structure and electrochemical properties of resynthesized NCA are further elucidated. Whereas the controlled Mg/Mn doping positively impacts both rate capability and cyclability, excessive Mn leads to degraded cyclability. Overall, our study expands the scope of resynthesis process of cathode active materials from spent LIBs, highlighting the intricate role of impurities and their impact on LIB performance.

Incorporation of Cu-TATAB metal-organic framework within polyurethane nanocomposite for enhanced thin film microextraction of some chlorinated pesticides

In this research, electrospun nanofibers based on copper-based metal organic framework (MOF)/polyurethane (PU) were prepared in order to achieve an applicable and superior extractive phase. The incorporation of MOF, in the synthesized nanocomposite contributed to the enhanced sorption efficiency. The prepared sorbent was implemented for the thin film microextraction (TFME) of target compounds with subsequent quantification using gas chromatography-mass spectrometry (GC–MS). To obtain the maximum efficiency of the synthesized sorbent, the influential parameters on extraction and desorption steps, including the MOF percentage in nanocomposite, desorption solvent type and its volume, desorption time, solution ionic strength and extraction time were optimized. After method development, the linear dynamic range (0.02–700 μg L−1), limits of detection (LODs) (0.005–0.1 μg L−1) and limits of quantification (LOQs))0.02–0.33 μg L−1(were calculated. The relative standard deviations values for intra-day and inter-day analysis were found to be in the range of 4.3–5.3 % and 6.2–8.1 %, respectively. The developed method was validated for the TFME of model organochlorine (OC) pesticide residues in fish, soil and water samples. the recovery values for the spiked samples at two concentration levels of 5 and 100 µg l-1 were found in the range of 72–110 %.

Comprehensive methodology for semi-volatile organic compound determination in ambient air with emphasis on polycyclic aromatic hydrocarbons analysis by GC–MS/MS

Polycyclic aromatic hydrocarbons are air pollutants that affect the human health and the environment, and their accurate determination in outdoor and indoor environments is important. This study presents a methodology for sampling and analysis of semi-volatile compounds in ambient air with emphasis on the polycyclic aromatic hydrocarbons, collected with low-volume pumps (4.8 m3) in unconditioned solid phase extraction cartridges (Isolute ENV+). Sampling in SPE cartridges with low-volume pumps allows the collection of both gas and particulate phase compounds in indoor as well as outdoor environments, and reduces the number of extraction steps required as well as the solvent volume used for extraction. Analysis of the 16 US-EPA priority PAHs after extraction was conducted by GC–MS/MS with recoveries of the PAHs 40–118 %. No breakthrough was detected during sampling. Moreover, the methodology includes storage test to assess the conservation of PAHs in the SPE cartridges in heat-sealable Kapac bags; simulating transport from sampling sites to laboratory, and storage under room, cold and frozen conditions at different time-intervals, up to 3 months after sampling. The results showed that concentration levels remained constant across various storage time intervals and temperatures, with naphthalene and acenaphthylene being the only exceptions, showing high blank levels for the first and losses at room temperature for the later. The method quantification limits, including sampling, storage and GC–MS/MS analysis ranged from 2000 pg m-3 for naphthalene and 300 pg m-3 for phenanthrene to less than 20.0 pg m-3 for higher molecular and less volatile PAHs, such as benzo[a]pyrene (LOQ = 8.0 pg m-3). The feasibility of the method was tested by sampling indoors under urban background air conditions, showing individual PAH concentrations 4 to 10 times higher than their method quantification limits.

A method for detecting gene doping in horse sports without DNA extraction

AbstractGene doping is prohibited in horse sports and can involve the administration of exogenous genes, called transgenes, to postnatal animals. Quantitative polymerase chain reaction (qPCR) methods have been developed to detect gene doping; however, these generally require DNA extraction from the plasma prior to qPCR. In this study, we developed two methods, direct droplet digital PCR (ddPCR) and nested ddPCR, to detect the equine erythropoietin (EPO) transgene without DNA extraction. Direct ddPCR used pretreated plasma and PCR to detect the EPO transgene spiked at 10 copies/μL. Nested ddPCR utilised pre‐amplification using nontreated plasma, purification of PCR products and PCR to detect the EPO transgene spiked at 1 copy/μL in plasma. These methods successfully detected the EPO transgene after intramuscular injection into horses. Since each method has different detection sensitivity, the combined use of direct ddPCR for screening and nested ddPCR for confirmation may complement each other and prevent the occurrence of false positives, allowing the reliable detection of gene‐doped substances. One advantage of these methods is the small amount of sample required, approximately 2.2–5.0 μl, owing to the lack of a DNA extraction step. Therefore, these tests could be applied to small volume samples as an alternative to conventional gene doping tests.

Valorization of spent catalyst of sponge iron production industries into high-purity metallic nickel via a novel three-step method of leaching, cementation and purification

In the present study, a three-step flowsheet combining hydrometallurgical extraction, electrochemical recovery, and purification steps is developed to investigate the possibility of recovering high-purity metallic nickel powder from the spent catalyst of the sponge iron production industry. In the extraction step, the Response Surface Methodology (RSM) is used to study and optimize the effect of temperature (25–85 °C), time (30–120 min), acid concentration (1–5 M), and NaCl concentration (0–0.16 g/mL) on the Ni and Al extraction efficiency with the help of the Design-Expert software. The optimal conditions for extracting 100 % Ni with 1.25 % Al impurity are 75 °C, 190 min, 3 M H2SO4, and 0.13 g/mL NaCl. During the electrochemical recovery step, Ni+2 ions from the leached solution were cemented by Al powder to produce metallic nickel powder. The solution’s initial pH, temperature, reaction time, and Al particle size are the process restrictions leading to a decrease in the efficiency and purity of the nickel powder. At this step, more than 99 % of the nickel ions, with more than 98 % purity, were obtained as magnetic nickel powder using the optimal temperature of 80 °C, and solution initial pH of 0.2, by using Al powder with less than 65 μm in particle size. At these operating conditions, the Al consumption was 7.5 times greater than the stoichiometric value. In the final step, the Al impurity in the synthetic nickel powder was removed using 1.5 M sodium hydroxide for 60 min, and the nickel powder was purified to 99.9 %. The ICP, SEM, and EDS analyses were used to determine the quality of the nickel powder.

Predicting the remaining useful life of rails based on improved deep spiking residual neural network

the remaining useful life (RUL) of rails is important to ensure safe and reliable operation of railway transportation lines. However, the severity of deterioration of rails from different types of damage varies. Multichannel vibrational data collected by sensors can be utilized to identify the relevant complex correlations and temporal relationships; however, existing methods struggle to predict rail damage accurately. Thus, this paper proposes a RUL prediction method for rails based on an improved deep spiking residual neural network. First, multichannel data collected by sensors are used directly as input to the predictive network without requiring a separate feature extraction step. Additionally, an encoding module is designed to adaptively convert the temporal sequences of data into spiking signals to reduce the information loss during the encoding process. Second, a spiking residual convolutional neural network (CNN) is incorporated into the proposed method to extract optimal features. The separable, spiking CNN can model the relationships among multichannel data accurately, and an attention mechanism is implemented to recalibrate the spiking feature maps such that the predictive network can distinguish between items of information effectively. Third, the spiking residual connections are altered to mitigate network degradation caused by the incompatibility between the conventional residual connections and the spiking neural network. Finally, the obtained spiking features are input to a fully connected layer to predict the rail RUL. Experimental results demonstrated that the proposed method can predict the rail RUL accurately, and validation results obtained on a rolling bearing dataset demonstrated the high generalizability of the proposed method.

Recovery of europium from E-waste using redox active tetrathiotungstate ligands

Rare-earth elements (REEs) are critical to our modern economy, yet their mining from natural ores bears a profound environmental impact. Traditional separation techniques are chemical and energy-intensive because their chemical similarities make REEs very challenging to purify, requiring multiple extraction steps to achieve high purity products. This emphasizes the need for sustainable and straightforward separation methods. Here we introduce a strategy for the direct separation of europium (Eu) from complex mixtures under ambient conditions, leveraging on the redox non innocence of purely inorganic tungsten tetrathiolate (WS42−) ligands. The recovery of Eu is achieved upon reduction of Eu(III) to a Eu(II) coordination polymer, driven by an induced internal electron transfer from the tetrathiotungstate ligand. Applying this strategy to unconventional feedstock such as spent energy-saving lamps allows selective europium recovery with separation factors over 1000 and recovery efficiency as high as 99% without pre-treatment of the waste.

A configuration for cooling assisted organic solvent coated thin film microextraction after dispersive liquid-liquid microextraction method: A microextraction method for ultra-trace analyzing of volatile sample

A combination of the dispersive liquid-liquid microextraction (DLLME) method based on the total vaporization procedure and cooling-assisted organic solvent-coated thin film microextraction (TFME) was applied for extracting chlorpyrifos (as the model compound). Based on the high thermal conductivity, a nickel foam thin film with the dimensions of 5.0 mm × 5.0 mm was used as a substrate for holding the organic solvent. Supporting thin film by organic solvent increases the thickness and contact area of the film relative to TFME or single drop microextraction (SDME) alone, resulting in a dramatic increase in the extraction efficiency. To protect the organic solvent and enhance the analyte distribution coefficient between the film and the vapor phase, a cooling system was applied. The proposed design was effective due to condensing the target analyte only on the uniform cooled thin film and not on the other regions in the extraction chamber. A corona discharge ionization source-ion mobility spectrometer was employed to identify the analyte. After optimizing the effective parameters, the limits of quantification (S/N = 10) and detection (S/N = 3) were calculated 0.1 and 0.03 μg L−1, respectively, and the dynamic range was measured between 0.1 and 7.0 μg L−1, with a determination coefficient of 0.9997. For three concentration levels of 0.1, 3.0, and 7.0 μg L−1, the relative standard deviations (n = 3) as the repeatability index were to be 6 %, 5 %, and 4 % for intra-day and 9 %, 6 %, and 5 % for inter-day, respectively. The enrichment factor was also calculated to be 3630 for the analyte concentration of 1.0 μg L−1. Well water, potato, and agricultural wastewater were analyzed as the real samples and the relative recovery values were measured between 92 % and 99 %. The accuracy of the proposed technique was validated by the European Standards EN 12393 method. In this approach, two steps of analyte extraction (DLLME and TFME) were used consecutively, resulting in better preconcentration and reduced matrix interference during cleaning-up.