Enhanced electrochemical performance of CuCo2S4/carbon nanotubes composite as electrode material for supercapacitors

CuCo2S4 is regarded as a promising electrode material for supercapacitor, but has inferior conductivity and poor cycle stability which restrict its wide-range applications. In this work, hierarchically hybrid composite of CuCo2S4/carbon nanotubes (CNTs) was synthesized using a facile hydrothermal and sulfuration process. The embedded CNTs in the CuCo2S4 matrix provided numerous effective paths for electron transfer and ion diffusion, and thus promoted the faradaic reactions of the CuCo2S4 electrode in the energy storage processes. The CuCo2S4/CNTs-3.2% electrode exhibited a significantly increased specific capacitance of 557.5 F g-1 compared with those of the pristine CuCo2S4 electrode (373.4 F g-1) and CuO/Co3O4/CNTs-3.2% electrode (356.5 F g-1) at a current density of 1 A g-1. An asymmetric supercapacitor (ASC) was assembled using the CuCo2S4/CNTs-3.2% as the positive electrode and the active carbon as the negative electrode, which exhibited an energy density of 23.2 Wh kg-1 at a power density of 402.7 W kg-1. Moreover, the residual specific capacitance of this ASC device retained 85.7 % of its original value after tested for 10000 cycles, indicating its excellent cycle stability

Atomically dispersed Cu-N3 on hollow spherical carbon nitride for acetaminophen degradation: Generation of 1O2 from H2O2

Discharge of recalcitrant pharmaceuticals into aquatic systems has caused severe impacts on public health and ecosystem. Advanced oxidation processes (AOPs) are effective for eliminating these refractory pollutants, for which single-atom catalysts (SACs) become the state-of-the-art materials owing to the maximized exposure of active metal sites. In this work, hollow spherical graphitic carbon nitride (hsCN) was fabricated to incorporate copper species to develop Fenton-like catalysts for acetaminophen (ACT) removal. Through pyrolysis of supramolecular assemblies derived from melamine-Cu complex and cyanuric acid, single atom Cu-N3 sites were anchored on hsCN by N-coordination to obtain SACu-hsCN. In virtue of the atomically dispersed Cu-N3 sites as well as the hollow structure of hsCN providing smooth channels for the interactions between single Cu atoms and reactants, the optimal 5.5SACu-hsCN removed 94.8% of ACT after 180 min of Fenton-like reactions, which was superior to that of 5.5AGCu-hsCN with aggregated Cu particles on hsCN (56.7% in 180 min). Moreover, 5.5SACu-hsCN was still active after four cycles of regeneration. The mechanism investigation demonstrated that both hydroxyl radicals (OH) and singlet oxygen (1O2) contributed to ACT degradation in 5.5SACu-hsCN/H202 system, in which non-radical 1O2 played the dominant role

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation

For an industrial process, the estimation of feeding composition is important for analyzing production status and making control decisions. However, random errors or even gross ones inevitably contaminate the actual measurements. Feeding composition is conventionally obtained via discrete and low-rate artificial testing. To address these problems, a feeding composition estimation approach based on data reconciliation procedure is developed. To improve the variable accuracy, a novel robust M-estimator is first proposed. Then, an iterative robust hierarchical data reconciliation and estimation strategy is applied to estimate the feeding composition. The feasibility and effectiveness of the estimation approach are verified on a fluidized bed roaster. The proposed M-estimator showed better overall performance

Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method

Walking is a basic requirement for participating in daily activities. Neurological diseases such as stroke can significantly affect one’s gait and thereby restrict one’s activities that are a part of daily living. Previous studies have demonstrated that gait temporal parameters are useful for characterizing post-stroke hemiparetic gait. However, no previous studies have investigated the symmetry, regularity and stability of post-stroke hemiparetic gaits. In this study, the dynamic time warping (DTW) algorithm, sample entropy method and empirical mode decomposition-based stability index were utilized to obtain the three aforementioned types of gait features, respectively. Studies were conducted with 15 healthy control subjects and 15 post-stroke survivors. Experimental results revealed that the proposed features could significantly differentiate hemiparetic patients from healthy control subjects by a Mann–Whitney test (with a p-value of less than 0.05). Finally, four representative classifiers were utilized in order to evaluate the possible capabilities of these features to distinguish patients with hemiparetic gaits from the healthy control subjects. The maximum area under the curve values were shown to be 0.94 by the k-nearest-neighbor (kNN) classifier. These promising results have illustrated that the proposed features have considerable potential to promote the future design of automatic gait analysis systems for clinical practice

Ni

Supercapacitors have become a research hotspot in the field of energy storage due to their highpower density, fast charging/discharging ability, long-term stability and safety. Nevertheless, relatively low energy density hindered their application. Herein, Ni3S2/Zn0.76Co0.24S (NZCS) microsphere were synthesized using a facile two-step hydrothermal process. The polymetallic synergies can improve conductivity and shorten ion transport path. The uniform particle distribution provided numerous active sites for faradaic reactions. The NZCS mircosphere showed a large capacity of 571.5 C g-1 at 1 A g-1 and 87% rate rentention when the current increases by 10 times. A hybrid supercapacitor assmebled by NZCS cathode and active carbon anode demostrate a high energy density of 44.1 Wh kg-1 (407.0 W kg-1) and a stable cycliability of 15,000 cycles with 15% loss. Thus, NZCS is a promising electrode material for high performance supercapacitor

The Role of Natural Language Processing in Cancer Care: A systematic scoping review with narrative synthesis

This protocol discusses the use of natural language processing (NLP) in cancer care. NLP is a tool that merges the fields of artificial intelligence and linguistics, focusing on text recognition, understanding, and generation. In cancer care, NLP can be used to extract and analyze information from various sources, including electronic health records, clinical notes, pathology reports, radiology reports, and social media data. This scoping review aims to explore the research and application of NLP in cancer treatment and patient self-management, identifying potential benefits and challenges to inform the development of more effective and efficient healthcare NLP systems. The review questions include the roles text data can provide in cancer patients' self-management, the type of support NLP technology can provide to cancer patients and their clinicians, and the challenges or barriers to using NLP technology to improve patient outcomes in oncology

Nanocomposites of cobalt sulfide embedded carbon nanotubes with enhanced supercapacitor performance

CoS is one of the ideal electrode materials for supercapacitor, but its long-term stability and electrochemical performance needed to be improved before its successful application. Uniformly embedding carbon nanotubes (CNTs) inside the CoS matrix can provide numerous and effective diffusion paths of electrons and electrolyte ions, which can reduce the charge-transfer resistance and effectively improve the electrochemical performance of CoS. In this work, nanocomposites of Co2(CO3)(OH)2 and CNTs were prepared using a facile hydrothermal method, and then were transformed into CoS1.29@CNTs nanocomposites via an ion-exchange process. The carbon nanotubes were uniformly embedded inside the CoS1.29 matrix. When the amount of CNTs was 6.1 wt%, the CoS1.29@CNTs electrode exhibited a higher specific capacitance (99.7 mAh g-1) than that of CoS1.29 electrode (84.1 mAh g-1) at a current density of 1 A g-1 measured in 2 M KOH electrolyte. The asymmetric supercapacitor assembled with the [email protected]% electrode and an activated carbon (AC) electrode exhibited an energy density of 39.1 Wh kg-1 at a power density of 399.9 W kg-1. Moreover, the specific capacitance of the [email protected]%//AC device maintained 91.3 % of its original value after 2000 cycles at a current density of 3 A g-1

Ultrafast Response/Recovery and High Selectivity of H2S Gas Sensor Based on α-Fe2O3 Nano-Ellipsoids from One-Step Hydrothermal Synthesis

Ultrafast response/recovery and high selectivity of gas sensors are critical for real-time and online monitoring of hazardous gases. In this work, α-Fe2O3 nano-ellipsoids were synthesized using a facile one-step hydrothermal method and investigated as highly sensitive H2S sensing materials. The nano-ellipsoids have an average long axis diameter of 275 nm and an average short axis diameter of 125 nm. H2S gas sensors fabricated using the α-Fe2O3 nano-ellipsoids showed excellent H2S sensing performance at an optimum working temperature of 260 ℃. The response and recovery times were 0.8 s/2.2 s for H2S gas with a concentration of 50 ppm, which are much faster than those of H2S gas sensors reported in literature. The α-Fe2O3 nano-ellipsoid based sensors also showed a high selectivity to H2S compared to other commonly investigated gases including NH3, CO, NO2, H2, CH2Cl2 and ethanol. In addition, the sensors exhibited high response values to different concentrations of H2S with a detection limit as low as 100 ppb, as well as excellent repeatability and long-term stability