Efficient rhodamine B dye photocatalytic degradation in aqueous media using novel ZnO nanomaterials co-doped with Ce and Dy.

Water pollution caused by dyes and industrial wastewater poses a significant threat to ecosystems. The purification of such pollutants presents a major challenge. Photocatalysis based on semiconductor materials is a potential wastewater treatment process due to its safety and cost-effectiveness. In the present work, Zn1-2xCexDyxO (x = 0.01-0.05) semiconductors were prepared by the sol-gel auto-ignition method. The samples are denoted CDZO1, CDZO3, and CDZO5 for x  = 0.01-0.05, respectively. The X-ray diffraction and Raman spectroscopy results revealed the formation of ZnO hexagonal phase wurtzite structure for all synthesized compositions. Different structural properties were determined. It was found that the lattice parameters and the unit cell volume increased, while the crystallite size diminished as x varied from 0.01 to 0.05. Transmission electron microscopy observations confirmed the formation of nanoparticles with the desired chemical compositions. The specific surface area (SSA) values are found to be 39.95 m2/g, 48.62 m2/g, and 51.36 m2/g for CDZO1, CDZO5, and CDZO5 samples, respectively. The reflectance spectra were recorded to examine the optical properties of the different nanoparticles. The values of the optical band gap were 3.221, 3.225, and 3.239 eV for CDZO1, CDZO3, and CDZO5 samples, respectively. In addition, the photocatalytic performance towards RhB dye degradation for the different samples was assessed. It was established that the CDZO3 sample with a moderate SSA value exhibited the superior photocatalytic performance among the other as-prepared samples wherein the percentage of degradation efficiency, and kinetic constant rate attained their maximum values of 98.22 % and 0.0521 min-1, respectively within 75 min. As per the obtained findings, it is evident that the Zn1-2xCexDyxO photocatalyst has prominent potential for use in the degradation of dyes and offers a useful route for impeding the recombination of electron-hole pairs of zinc oxide material.

Nuclear quantum effects in the acetylene:ammonia plastic co-crystal.

Organic molecular solids can exhibit rich phase diagrams. In addition to structurally unique phases, translational and rotational degrees of freedom can melt at different state points, giving rise to partially disordered solid phases. The structural and dynamic disorder in these materials can have a significant impact on the physical properties of the organic solid, necessitating a thorough understanding of disorder at the atomic scale. When these disordered phases form at low temperatures, especially in crystals with light nuclei, the prediction of material properties can be complicated by the importance of nuclear quantum effects. As an example, we investigate nuclear quantum effects on the structure and dynamics of the orientationally disordered, translationally ordered plastic phase of the acetylene:ammonia (1:1) co-crystal that is expected to exist on the surface of Saturn's moon Titan. Titan's low surface temperature (∼90 K) suggests that the quantum mechanical behavior of nuclei may be important in this and other molecular solids in these environments. By using neural network potentials combined with ring polymer molecular dynamics simulations, we show that nuclear quantum effects increase orientational disorder and rotational dynamics within the acetylene:ammonia (1:1) co-crystal by weakening hydrogen bonds. Our results suggest that nuclear quantum effects are important to accurately model molecular solids and their physical properties in low-temperature environments.

Dataset for boiling acoustic emissions: A tool for data driven boiling regime prediction.

Boiling is used for the thermal management of high-energy-density devices and systems. However, sudden thermal runaway at boiling crisis often results in catastrophic failures. Machine learning is a promising tool for in-situ monitoring of boiling-based systems for preemptive control of boiling crisis. A carefully acquired and well-labeled dataset is a primary requirement for utilizing any data-driven learning framework to extract valuable descriptors. Here, we present a comprehensive dataset of boiling acoustics presented in our recent work [1]. We collect the audio files through meticulously controlled near-saturated pool boiling experiments under steady-state conditions. To this end, we connect a high-sensitivity hydrophone to a pre-amplifier and a data acquisition unit for accurate and reliable acquisition of acoustic signals. We organize the audio files into four categories as per the respective boiling regimes: background or natural convection (BKG, 2-5W/cm2), nucleate boiling (NB, 8-140W/cm2), excluding those at higher heat flux values preceding the onset of boiling crisis or the critical heat flux (Pre-CHF, ≈145W/cm2), and transition boiling (TB, uncontrolled). Each audio file label provides explicit information about the heat flux value and the experimental conditions. This dataset, consisting of 2056 files for BKG, 13367 files for NB, 399 files for Pre-CHF, and 460 files for TB, serves as the foundation for training and evaluating a deep learning strategy to predict boiling regimes. The dataset also includes acoustic emission data from transient pool boiling experiments conducted with varying heating strategies, heater surface, and boiling fluid modifications, creating a valuable dataset for developing robust data-driven models to predict boiling regimes. We also provide the associated MATLAB® codes used to process and classify these audio files.

Artificial Neural Network-Aided Computational Approach for Mechanophenotyping of Biological Cells Using Atomic Force Microscopy.

The artificial neural network (ANN) based models have shown the potential to provide alternate data-driven solutions in disease diagnostics, cell sorting and overcoming AFM-related limitations. Hertzian model-based prediction of mechanical properties of biological cells, although most widely used, has shown to have limited potential in determining constitutive parameters of cells of uneven shape and nonlinear nature of force-indentation curves in AFM-based cell nano-indentation. We report a new artificial neural network-aided approach, which takes into account, the variation in cell shapes and their effect on the predictions in cell mechanophenotyping. We have developed an artificial neural network (ANN) model which could predict the mechanical properties of biological cells by utilizing the force versus indentation curve of AFM. For cells with 1 µm contact length (platelets), we obtained a recall of 0.97 ± 0.03 and 0.99 ± 0.0 for cells with hyperelastic and linear elastic constitutive properties respectively with a prediction error of less than 10%. Also, for cells with 6-8 µm contact length (red blood cells), we obtained the recall of 0.975 in predicting mechanical properties with less than 15% error. We envisage that the developed technique can be used for better estimation of cells' constitutive parameters by incorporating cell topography into account.

A systematic review of methodologies and techniques for integrating ergonomics into development and assessment of manually operated equipment.

Objective. Humans have limited power and require mechanization, to meet high energy demand, through equipment. We initiated this study to accumulate data from previous research to identify critical issues and approaches in implementing ergonomic principles into the design, intervention, development and assessment of manually operated equipment. Method. The literature search was carried out in scientific databases: Scopus and PubMed. Fifty-three research articles, meeting the inclusion criteria, were selected for this review. Results. The study indicated a propensity of countries with lower-middle-income and high-income groups, and of the agricultural and manufacturing sector toward research and development of manually operated equipment. A thorough study of the equipment design process revealed that health and safety was the prime motivator in the pre-design phase, an experimental prototype approach was most utilized in the design phase and a direct measurement technique was most frequently used in the post-design phase. Conclusion. The study highlights the scarcity of research in the integration of ergonomics into the design of manually operated equipment among countries with the low-income group. This study also promotes the use of virtual design and assessment techniques for cost-effectiveness.

Endorsing a Hidden Plasmonic Mode for Enhancement of LSPR Sensing Performance in Evolved Metal-insulator Geometry Using an Unsupervised Machine Learning Algorithm.

Large-area nanoplasmonic structures with pillared metal-insulator geometry, also called nanomushrooms (NM), consist of an active spherical-shaped plasmonic material such as gold as its cap and silicon dioxide as its stem. NM is a geometry which evolves from its precursor, nanoislands (NI) consisting of aforementioned spherical structures on flat silicon dioxide substrates, via selective physical or chemical etching of the silicon dioxide. The NM geometry is well-known to provide enhanced localized surface plasmon resonance (LSPR) sensitivity in biosensing applications as compared to NI. However, precise optical phenomenon behind this enhancement is unknown and often associated with the existence of electric fields in the large fraction of the spatial region between the pillars of NM, usually accessible by the biomolecules. Here, we uncover the association of LSPR enhancement in such geometries with a hidden plasmonic mode by conducting magneto-optics measurements and by deconvoluting the absorbance spectra obtained during the local refractive index change of the NM and NI geometries. By the virtue of principal component analysis, an unsupervised machine learning technique, we observe an explicit relationship between the deconvoluted modes of LSPR, the differential absorption of left and right circular polarized light, and the refractive index sensitivity of the LSPR sensor. Our findings may lead to the development of new approaches to extract unknown properties of plasmonic materials or establish new fundamental relationships between less understood photonic properties of nanomaterials.

Doping Independent Work Function and Stable Band Gap of Spinel Ferrites with Tunable Plasmonic and Magnetic Properties.

Tuning optical or magnetic properties of nanoparticles, by addition of impurities, for specific applications is usually achieved at the cost of band gap and work function reduction. Additionally, conventional strategies to develop nanoparticles with a large band gap also encounter problems of phase separation and poor crystallinity at high alloying degree. Addressing the aforementioned trade-offs, here we report Ni-Zn nanoferrites with energy band gap (Eg) of ≈3.20 eV and a work function of ≈5.88 eV. While changes in the magnetoplasmonic properties of the Ni-Zn ferrite were successfully achieved with the incorporation of bismuth ions at different concentrations, there was no alteration of the band gap and work function in the developed Ni-Zn ferrite. This suggests that with the addition of minute impurities to ferrites, independent of their changes in the band gap and work function, one can tune their magnetic and optical properties, which is desired in a wide range of applications such as nanobiosensing, nanoparticle based catalysis, and renewable energy generation using nanotechnology.

Graphene Oxide Nanoparticles Modified Paper Electrode as a Biosensing Platform for Detection of the htrA Gene of O. tsutsugamushi.

The unique structural and electrochemical properties of graphene oxide (GO) make it an ideal material for the fabrication of biosensing devices. Therefore, in the present study, graphene oxide nanoparticles modified paper electrodes were used as a low-cost matrix for the development of an amperometric DNA sensor. The graphene oxide was synthesized using the modified hummers method and drop cast on a screen-printed paper electrode (SPPE) to enhance its electrochemical properties. Further, the GO/SPPE electrode was modified with a 5'NH2 labeled ssDNA probe specific to the htrA gene of Orientia tsutsugamushi using carbodiimide cross-linking chemistry. The synthesized GO was characterized using UV-Vis, FTIR, and XRD. The layer-by-layer modification of the paper electrode was monitored via FE-SEM, cyclic voltammetry, and electrochemical impedance spectroscopy (EIS). The sensor response after hybridization with single-stranded genomic DNA (ssGDNA) of O. tsutsugamushi was recorded using differential pulse voltammetry (DPV). Methylene blue (1 mM in PBS buffer, pH 7.2) was used as a hybridization indicator and [Fe(CN)6]-3/-4 (2.5 mM in PBS buffer, pH 7.2) as a redox probe during electrochemical measurements. The developed DNA sensor shows excellent sensitivity (1228.4 µA/cm2/ng) and LOD (20 pg/µL) for detection of O. tsutsugamushi GDNA using differential pulse voltammetry (DPV).

Nanoferrites heterogeneous catalysts for biodiesel production from soybean and canola oil: a review.

Fossil fuel depletion and pollution are calling for alternative, renewable energies such as biofuels. Actual challenges include the design of efficient processes and catalysts to convert various feedstocks into biofuels. Here, we review nanoferrites heterogeneous catalysts to produce biodiesel from soybean and canola oil. For that, transesterification is the main synthesis route and offers simplicity, cost-effectiveness, better process control, and high conversion yield. Catalysis with nanoferrites and composites allow to obtain yields higher than 95% conversion with less than 5.0 wt.% of catalyst loading at 80 °C in 1-2 h. More than 90% conversion yields can be achieved with a moderate alcohol/oil molar ratio, i.e., between 12:1 to 16:1. Catalyst recovery is easy due to the magnetic properties of nanoferrite, which can be effectively reused up to 4 times with less than 10% loss of catalytic efficiency.

AuNPs/CNF-modified DNA biosensor for early and quick detection of O. tsutsugamushi in patients suffering from scrub typhus.

A novel approach has been developed for the detection of 56 kDa tissue-specific antigen (TSA) gene of Orientia tsutsugamushi a causative agent of scrub typhus disease. The approach was developed by immobilization of 5' NH2 labeled ssDNA probe selective to 56 kDa TSA gene, to the surface of AuNPs/CNF modified screen-printed electrode. An electrochemical response was recorded with single stranded genomic DNA (ssDNA) of O. tsutsugamushi isolated from patient sample, using cyclic voltammetry and electrochemical impedance spectroscopy. The electrode surface was characterized by Field-Emission Scanning electron microscope (FE-SEM), Fourier Transform Infrared Spectroscopy (FTIR) and Raman Spectroscopy at each step of fabrication. The DNA biosensor shows optimum response within 50-60 s at room temperature (25 ± 3 °C). The sensor shows higher sensitivity [7849 (µA/cm2)/ng DNA], fast response time (60 s), wider linear range (0.04-2.6 ng) with limit of detection of 0.02 ng/µl of ssDNA sample.