Particle Density Research Articles

Reconstruction of particle distribution for tomographic particle image velocimetry based on unsupervised learning method

The development of deep learning has inspired some new methods to solve the 3D reconstruction problem for Tomographic Particle Image Velocimetry (Tomo-PIV). However, the supervised learning method requires a large number of data with ground truth as training information, which is very difficult to gather from experiments. Although synthetic datasets can be used as alternatives, they are still not exactly the same with the real-world experimental data. In this paper, an Unsupervised Reconstruction Technique based on U-net (UnRTU) is proposed to reconstruct volume particle distribution explicitly. Instead of using ground truth data, a projection function is used as an unsupervised loss function for network training to reconstruct particle distribution. The UnRTU was compared with some traditional algebraic reconstruction algorithms and supervised learning method using synthetic data under different particle density and noise level. The results indicate that UnRTU outperforms these traditional approaches in both reconstruction quality and noise robustness, and is comparable to the supervised learning methods AI-PR. For experimental tests, particles dispersed in cured epoxy resin are moved by an electric rail with a certain speed to obtain the ground truth data of particle velocity. Compared with other algorithms, the reconstructed particle distribution by UnRTU has the best reconstruction fidelity. And the accuracy of the 3D velocity field estimated by UnRTU is 12.9% higher than that from the traditional MLOS-MART algorithm. It demonstrates significant potential and advantages for UnRTU in 3D reconstruction of particle distribution. Finally, UnRTU was successfully applied to the high-speed planar cascade airflow field, demonstrating its applicability for measuring complex fluid flow fields at higher particle density.

Effect of Different Levels of NPK, Biochar and Azotobacter on Physico-chemical Properties of Soil on Cowpea (Vigna unguiculata)

In light of this, the following goals are present in the experiment "Effect of Different Levels of NPK and Biochar, Azotobacter on Physico-chemical Properties of Soil and Yield Attributes of Cowpea to calculate the impact of various NPK, Biochar, and Azotobacter dosages on the physical-chemical characteristics of soil. An excavated soil sample from the experimental site revealed that the land topography ranged from nearly level to sloped by 1% to 6%, with soil area falling into the Inceptisol order. The soil texture was sandy loam, with sand percentages of 62.65%, silt percentages of 21.09, and clay percentages of 16.26. The pH of soil was 6.89, and its electrical conductivity (EC) was non-saline (0.42 ds m-1). Organic carbon content was low to medium, available nitrogen was low to medium (280.78 kg ha-1), available phosphorus was 17.34 kg ha-1, and available potassium was 168.16 kg ha-1. Two factors with three levels of @NPK 0, 50, and 100% ha-1, three levels of @Biochar 0, 50, and 100% ha-1, and a randomized block design were used in the statistical analysis. During field testing, nine different treatments were used; the best outcomes were significant. The results indicate that the physical and chemical parameters of the soil, including the cumulative mean values for bulk density (1.39 and 1.41 mg m-3), particle density (2.46 and 2.47 mg m-3), and soil pH (6.89 and 6.91), attained their maximum in T1 (Absolute control) at depths of 0-15 cm and 15-30 cm, respectively. Additionally, the percentage pore space was found to be 48.22% and 47.39%, and the water holding capacity was found to be 44.14% and 45.34%), electrical conductivity (EC) was measured at 0.49 ds m-1 and 0.52 ds m-1, the percentage of organic carbon was determined to be 0.40%), and the available nitrogen was found to be (299.78 kg ha-1 and 295.76 kg ha-1, available phosphorus was (23.78 kg ha-1 and 21.98 kg ha-1), and the available potassium was (179.25 kg ha-1 and 176.56 kg ha-1).

Area-Selective Atomic Layer Deposition of Ru Using Carbonyl-Based Precursor and Oxygen Co-Reactant: Understanding Defect Formation Mechanisms.

Area selective deposition (ASD) is a promising IC fabrication technique to address misalignment issues arising in a top-down litho-etch patterning approach. ASD can enable resist tone inversion and bottom-up metallization, such as via prefill. It is achieved by promoting selective growth in the growth area (GA) while passivating the non-growth area (NGA). Nevertheless, preventing undesired particles and defect growth on the NGA is still a hurdle. This work shows the selectivity of Ru films by passivating the Si oxide NGA with self-assembled monolayers (SAMs) and small molecule inhibitors (SMIs). Ru films are deposited on the TiN GA using a metal-organic precursor tricarbonyl (trimethylenemethane) ruthenium (Ru TMM(CO)3) and O2 as a co-reactant by atomic layer deposition (ALD). This produces smooth Ru films (<0.1 nm RMS roughness) with a growth per cycle (GPC) of 1.6 Å/cycle. Minimizing the oxygen co-reactant dose is necessary to improve the ASD process selectivity due to the limited stability of the organic molecule and high reactivity of the ALD precursor, still allowing a Ru GPC of 0.95 Å/cycle. This work sheds light on Ru defect generation mechanisms on passivated areas from the detailed analysis of particle growth, coverage, and density as a function of ALD cycles. Finally, an optimized ASD of Ru is demonstrated on TiN/SiO2 3D patterned structures using dimethyl amino trimethyl silane (DMA-TMS) as SMI.

CLT for NESS of a reaction-diffusion model

We study the scaling properties of the non-equilibrium stationary states (NESS) of a reaction-diffusion model. Under a suitable smallness condition, we show that the density of particles satisfies a law of large numbers with respect to the NESS, with an explicit rate of convergence, and we also show that at mesoscopic scales the NESS is well approximated by a local equilibrium (product) measure, in the total variation distance. In addition, in dimensions d≤3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d \\le 3$$\\end{document} we show a central limit theorem for the density of particles under the NESS. The corresponding Gaussian limit can be represented as an independent sum of a white noise and a massive Gaussian free field, and in particular it presents macroscopic correlations.

Impact of various DIII-D diagnostics on the accuracy of neural network surrogates for kinetic EFIT reconstructions

Kinetic equilibrium reconstructions make use of profile information such as particle density and temperature measurements in addition to magnetics data to compute a self-consistent equilibrium. They are used in a multitude of physics-based modeling. This work develops a multi-layer perceptron (MLP) neural network (NN) model as a surrogate for kinetic Equilibrium Fitting (EFITs) and trains on the 2019 DIII-D discharge campaign database of kinetic equilibrium reconstructions. We investigate the impact of including various diagnostic data and machine actuator controls as input into the NN. When giving various categories of data as input into NN models that have been trained using those same categories of data, the predictions on multiple equilibrium reconstruction solutions (poloidal magnetic flux, global scalars, pressure profile, current profile) are highly accurate. When comparing different models with different diagnostics as input, the magnetics-only model outputs accurate kinetic profiles and the inclusion of additional data does not significantly impact the accuracy. When the NN is tasked with inferring only a single target such as the EFIT pressure profile or EFIT current profile, we see a large increase in the accuracy of the prediction of the kinetic profiles as more data is included. These results indicate that certain MLP NN configurations can be reasonably robust to different burning-plasma-relevant diagnostics depending on the accuracy requirements for equilibrium reconstruction tasks.

Dependence of thermodynamic quantities at freeze-out on pseudorapidity and collision energy in p-p collisions at LHC energies* *Supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R106), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. In addition, the authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research

The transverse momentum distributions of charged hadrons produced in proton-proton collisions at center-of-mass energies ( ) of 0.9 TeV and 2.36 TeV, as measured by the CMS detector at the Large Hadron Collider (LHC), have been analyzed within various pseudorapidity classes utilizing the thermodynamically consistent Tsallis distribution. The fitting procedure resulted in the key parameters, namely, effective temperature (T), non-extensivity parameter (q), and kinetic freezeout volume (V). Additionally, the mean transverse momentum ( ) and initial temperature (Ti ) of the particle source are determined through the fit function and string percolation method, respectively. An alternative method is employed to calculate the kinetic freezeout temperature ( ) and transverse flow velocity ( ) from T. Furthermore, thermodynamic quantities at the freezeout, including energy density (ε), particle density (n), entropy density (s), pressure (P), and squared speed of sound ( ), are computed using the extracted T and q. It is also observed that, with a decrease in pseudorapidity, all thermodynamic quantities except V and q increase. This trend is attributed to greater energy transfer along the mid pseudorapidity. q increases towards higher values of pseudorapidity, indicating that particles close to the beam axis are far from equilibrium. Meanwhile, V remains nearly independent of pseudorapidity. The excitation function of these parameters (q) shows a direct (inverse) correlation with collision energy. The ε, n, s, and P show a strong dependence on collision energies at low pseudorapidities. Explicit verification of the thermodynamic inequality suggests the formation of a highly dense droplet-like Quark-Gluon Plasma (QGP). Additionally, the inequality is explicitly confirmed, aligning with the evolution of the produced fireball.

A study on the occurrence modes and distribution characteristics of sulfur and mercury in coal gasification slag

Although coal gasification is recognized as a core technology for clean coal utilization, S and Hg can migrate to gas, wastewater, and slag during the gasification process, causing environmental pollution. It is well known that S and Hg have a strong affinity. Therefore, the interaction between S and Hg during the gasification process warrants further investigation. In this study, the occurrence modes and distribution characteristics of S and Hg in gasification slag were systematically investigated using multiple treatments and various characterization techniques. The results showed that sulfur in the gasification slag exists predominantly as organic sulfur, while mercury mainly exists as HgS/silicate-bound Hg. Although the relative enrichment index of Hg (RE < 0.7) indicates its high volatility, a more pronounced enrichment effect for mercury of fine slag was indicated by its much higher RE than coarse slag. When exploring the relationship among the contents of C, S, and Hg and specific surface area in gasification slag, it was found that the correlation between S and Hg content in fine slag was the strongest, with a correlation coefficient (R2) of 0.96, indicating a significant influence of sulfur content in fine slag on mercury content. Additionally, it was observed that the S and Hg contents in gasification slag increased with the decrease of particle size and density. These results provide an important understanding of the distribution, migration, and transformation behavior of S and Hg during coal gasification, as well as a certain theoretical support and technical basis for the resource utilization of gasification slag.

Experimental Study on Proppant Migration in Fractures Following Hydraulic Fracturing

Complex fracture technology is key to the successful development of unconventional oil and gas reservoirs, such as shale. Most current studies focus on how to improve the complexity of the fracture network. It is still unclear whether proppant can enter the branch fractures at all levels after the formation of complex fractures. The effects of construction displacement, proppant particle size, proppant density, fracturing fluid viscosity, sand ratio, and other factors on proppant migration in single fractures and complex fractures were studied using an experimental device independently developed by the laboratory. The results show that the lowest point height of the sandbank and the equilibrium height of the sandbank are directly proportional to the particle concentration and density, respectively, and inversely proportional to the displacement and fracturing fluid viscosity. The equilibrium time of the sandbank is inversely proportional to the displacement, particle concentration, and density, respectively, and proportional to the viscosity of the fracturing fluid. Under the same experimental conditions, the larger the branch angle, the smaller the height of the main/secondary fracture sandbank. In the design of the fracturing process, fracturing fluid with varying viscosities and proppant with different densities should be selected according to the formation conditions and fracturing targets. In the face of long fracture lengths, the combination of low-viscosity fracturing fluid with an appropriate viscosity and low-density proppant can meet the goal of placing proppant over long distances and effectively supporting fractures over extended lengths. Subsequently, high-density proppant or reduced construction displacement are adopted to usefully support fractures in the near-wellbore area. The results of this paper can provide theoretical support for proppant selection and fracturing program design.

Unbalanced Optimal Transport For Stochastic Particle Tracking

Non-invasive flow measurement techniques, such as particle tracking velocimetry, resolve 3D velocity fields by pairing tracer particle positions in successive time steps. These trajectories are crucial for evaluating physical quantities like vorticity, shear stress, pressure, and coherent structures. Traditional approaches deterministically reconstruct particle positions and extract particle tracks using tracking algorithms. However, reliable track estimation is challenging due to measurement noise caused by high particle density, particle image overlap, and falsely reconstructed 3D particle positions. To overcome this challenge, probabilistic approaches quantify the epistemic uncertainty in particle positions, typically using a Gaussian probability distribution. However, the standard deterministic tracking algorithms relying on nearest-neighbor search do not directly extend to the probabilistic setting. Moreover, such algorithms do not necessarily find globally consistent solutions robust to reconstruction errors. This paper aims to develop a globally consistent nearest-neighborhood algorithm that robustly extracts stochastic particle tracks from the reconstructed Gaussian particle distributions in all frames. Our tracking algorithm relies on the unbalanced optimal transport theory in the metric space of Gaussian measures. Specifically, we optimize a binary transport plan for efficiently moving the Gaussian distributions of reconstructed particle positions between time frames. We achieve this by computing the partial Wasserstein distance in the metric space of Gaussian measures. Our tracking algorithm is robust to position reconstruction errors since it automatically detects the number of particles that should be matched through hyperparameter optimization. Notably, our tracking algorithm also readily applies to the standard deterministic PTV case. Finally, we validate our method using an in vitro flow experiment using a 3D-printed cerebral aneurysm.

Assimilating Sparse 3D-PTV Data Of A Pulsed Jet Using Physics-Informed Neural Networks

Phase-resolved volumetric velocity measurements of a pulsed jet are conducted by means of three-dimensional particle tracking velocimetry (PTV). The resulting scattered and relatively sparse data are densely reconstructed by adopting physics-informed neural networks (PINNs), here regularized by the Navier-Stokes equations. It is shown that the assimilation yields a higher spatial resolution, and the process remains robust, even at low particle densities ( 𝑝𝑝𝑝 &lt; 0.001). This is achieved by enforcing compliance with the governing equations, thus leveraging the spatiotemporal evolution of the measured flow field. The results indicate that the PINN reconstructs unambiguously velocity, vorticity and pressure fields with a level of detail not attainable with conventional methods (binning) or more advanced data assimilation techniques (vortex-in-cell). The results of this article support the findings of Clark di Leoni (2023) suggesting that the PINN methodology is inherently suited to the assimilation of PTV data, in particular under conditions of severe sparsity or during experiments with limited control of seeding concentration.

Interplay between short-range and critical long-range fluctuations in the out-of-equilibrium behavior of the particle density at quantum transitions

Interplay between short-range and critical long-range fluctuations in the out-of-equilibrium behavior of the particle density at quantum transitions

Designing highly efficient interlocking interactions in anisotropic active particles

Cluster formation of microscopic swimmers is key to the formation of biofilms and colonies, efficient motion and nutrient uptake, but, in the absence of other interactions, requires high swimmer concentrations to occur. Here we experimentally and numerically show that cluster formation can be dramatically enhanced by an anisotropic swimmer shape. We analyze a class of model microswimmers with a shape that can be continuously tuned from spherical to bent and straight rods. In all cases, clustering can be described by Michaelis-Menten kinetics governed by a single scaling parameter that depends on particle density and shape only. We rationalize these shape-dependent dynamics from the interplay between interlocking probability and cluster stability. The bent rod shape promotes assembly in an interlocking fashion even at vanishingly low particle densities and we identify the most efficient shape to be a semicircle. Our work provides key insights into how shape can be used to rationally design out-of-equilibrium self-organization, key to creating active functional materials and processes that require two-component assembly with high fidelity.

Study on particle size and density segregation law in the separating process of poor quality coal with high gangue content under the action of composite force field

ABSTRACT Thin-seam mining frequently captures a significant proportion of gangue and interbedded coal in surface coal mines. Consequently, raw coal has a broad range of particle size distributions and a high percentage of components with intermediate densities, which are complex and challenging to separate. This study aims to determine the method of product division and the segregation law of particle size and density of raw coal during the separation process using a vibration and airflow composite force field. A difference in the particle size segregation of the coal particles was evident. Particle-size segregation primarily occurs in the separation area. Large-particle-size coal has a wide distribution range, spreading over the entire bed and fully utilizing the bed to achieve separation. The material becomes increasingly displaced into the separation area as its particle size decreases, thereby weakening the separation effect. Large coal particles can undergo density segregation in the vertical direction, with low-density material placed in the upper layer. This ensures that low-density material is incorporated into the refined coal product. Regardless of density, the coal tended to tilt more toward the bottom layer as the particle size decreased. This results in a poor density stratification effect with refined coal products mixed with gangue and an increase in the mismatch content. The extent of fine coal discharge was determined to be two-fifths of the entire length of the discharge side. This ensures that the majority of refined coal is recovered and that there is no excessive gangue mixing with the refined coal output. Achieving a 55.54% yield, refined coal lowers its ash content by 16%, increases its calorific value by 1400 kcal/kg, and effectively separates coal with a high gangue concentration.

Improvement of storage stability and bioaccessibility of microencapsulated black carrot (Daucus Carota ssp. sativus) anthocyanins using maltodextrin and sericin protein combinations as wall material

This study explores the potential of microencapsulation to enhance the stability and bioaccessibility of anthocyanins from black carrots (Daucus carota ssp. sativus). Anthocyanins, known for their health benefits, are often limited in their application due to poor stability and bioaccessibility. We investigated microencapsulation using maltodextrin alone (BAM) and in combination with sericin proteins (BAMS1, BAMS2, BAMS3), a novel approach. Spray drying was employed to encapsulate anthocyanins, with maltodextrin (20%) and varying concentrations of sericin protein (1%, 2%, 3%) as wall materials. Comprehensive analysis encompassed parameters such as production yield, moisture content, bulk and tapped density, encapsulation efficiency, particle size distribution, surface morphology, FTIR, amino acid profiling, storage stability under diverse temperature conditions (4 °C, 25 °C, 37 °C), and simulated gastrointestinal conditions to assess bioaccessibility. Results indicated a range of production yield (51.05%–59.13%) and encapsulation efficiency (87.68%–94.01%), with moisture content between 5.24% and 6.47%. Sericin incorporation notably influenced moisture content and particle density. Particle size varied from 74.14 μm to 160 μm, with discernible differences attributed to sericin concentration. SEM revealed smooth-continuous surface morphologies, indicating the influence of sericin on capsule structure. Preservation of essential amino acid profiles underscored the efficacy of the encapsulation process. Enhanced stability and degradation kinetic parameters of anthocyanins, particularly evident in BAMS3 microcapsules, underscored the protective role of sericin. In vitro digestion studies confirmed controlled release dynamics, highlighting the potential for improved anthocyanin bioaccessibility facilitated by the sericin-maltodextrin composite encapsulation approach.

Mathematical simulation of ionospheric plasma diagnostics by electric current measurements using an insulated probe system

The goal of this work is to theoretically substantiate the possibility of determining the charged particle density in the ionospheric plasma by separately measuring the electric currents of an insulated probe system in the electron saturation region. The ionospheric plasma composition is modeled by two ion species with significantly different masses and electrons to keep the plasma quasi-neutrality. The probe system, which is electrically insulated from the spacecraft structure, consists of cylindrical electrodes: a probe and a reference electrode. The reference electrode to probe current-collecting area ratio can be significantly less than required by the single cylindrical probe theory. The electrodes are oriented transversely to a supersonic flow of a collisionless plasma. In addition to the main plasma with two ion species, a model plasma with a single ion species is considered. The mass of the model ions is such that the ion saturation current to the cylinder is the same for both plasma models. Based on a previously obtained asymptotic solution for the electron saturation current in a plasma with a single ion species, computational formulas are found for determining the ion mass composition and the electron density by probe current measurements. The errors of the formulas are estimated numerically and analytically as a function of the probe system geometry, the bias potential of the probe relative to the reference electrode, and the accuracy of potential and current measurements. It is shown that a proper choice of the probe system settings and the accuracy of probe measurements assures a reliable determination of the charged particle densities in a plasma with two ion species. A priori estimates are presented for the effect of the current bias potential measurement errors on the reliability of determining the ion mass composition and the electron density of the ionospheric plasma.

Discrete Element Method Simulation of Particulate Material Fracture Behavior on a Stretchable Single Filter Fiber with Additional Gas Flow

This study presents a comprehensive discrete element method (DEM) simulation approach for the stretching of a filter fiber with a separated polydisperse particle structure on top. For a realistic interaction between the fiber surface and the particles, the original surface of the polymer fiber was projected onto the surface of the fiber cylinder using surface imaging technologies (atomic force microscopy (AFM) and white-light interferometry). In addition, the adhesive forces between particle–fiber and particle–particle contacts were calibrated in the DEM domain using values from self-conducted AFM measurements. Fiber stretching was implemented by the linear motion of small periodic fiber elements. Discretization problems were resolved through studying the stretching of a fiber segment at the size of 8 mm. A critical fiber element length was discovered to be ≈100 μm for minimizing discretization dependencies during the cracking of the particle structure. The number and density of particle–particle contacts within the particle loading on the fiber were obtained at two different elongation rates. Effects such as densification of the particulate structure and increased detachment due to additional air flow were demonstrated.

Coupling molecular density functional theory with converged selected configuration interaction methods to study excited states in aqueous solution.

This paper presents the first implementation of a coupling between advanced wavefunction theories and molecular density functional theory (MDFT). This method enables the modeling of solvent effect into quantum mechanical (QM) calculations by incorporating an electrostatic potential generated by solvent charges into the electronic Hamiltonian. Solvent charges are deduced from the spatially and angularly dependent solvent particle density. Such a density is obtained through the minimization of the functional associated with the molecular mechanics (MM) Hamiltonian describing the interaction between the fluid particles. The introduced QM/MDFT framework belongs to QM/MM family of methods, but its originality lies in the use of MDFT as the MM solver, offering two main advantages. First, its functional formulation makes it competitive with respect to sampling-based molecular mechanics. Second, it preserves a molecular-level description lost in macroscopic continuum approaches. The excited state properties of water and formaldehyde molecules solvated into water have been computed at the selected configuration interaction (SCI) level. The excitation energies and dipole moments have been compared with experimental data and previous theoretical work. A key finding is that using the Hartree-Fock method to describe the solute allows for predicting the solvent charge around the ground state with sufficient precision for the subsequent SCI calculations of excited states. This significantly reduces the computational cost of the described procedure, paving the way for the study of more complex molecules.

Decoupling of rotation and translation at the colloidal glass transition.

In dense particle systems, the coupling of rotation and translation motion becomes intricate. Here, we report the results of confocal fluorescence microscopy where simultaneous recording of translational and rotational particle trajectories from a bidisperse colloidal dispersion is achieved by spiking the samples with rotational probe particles. The latter consist of colloidal particles containing two fluorescently labeled cores suited for tracking the particle's orientation. A comparison of the experimental data with event driven Brownian simulations gives insights into the system's structure and dynamics close to the glass transition and sheds new light onto the translation-rotation coupling. The data show that with increasing volume fractions, translational dynamics slows down drastically, whereas rotational dynamics changes very little. We find convincing agreement between simulation and experiments, even though the simulations neglect far-field hydrodynamic interactions. An additional analysis of the glass transition following mode coupling theory works well for the structural dynamics but indicates a decoupling of the diffusion of the smaller particle species. Shear stress correlations do not decorrelate in the simulated glass states and are not affected by rotational motion.

The role of microbubble dose in combined microflotation of fine particles

Flotation of small particles is one of the global challenges facing the mineral raw materials processing industry. Large amounts of non-ferrous and rare metals are lost in the flotation tailings in the form of mineral particles below 15 µm in size as a result of the low effectiveness of their capture by coarse bubbles generated in conventional flotation machines. The method of combined microflotation, developed in recent years, uses conventional coarse bubbles (CB) and microbubbles (MB) produced in the stand-alone generator of air-in-water microdispersion, which serves as the flotation carriers. Depending on the MB dose, the effect of their application may be positive or negative. The theoretical analysis of various mechanisms of particle transfer onto the surface of coarse bubbles and further into the froth layer allowed to obtain the formula for the optimal MB dose f=d&lt;sub&gt;d&lt;/sub&gt;/2d&lt;sub&gt;p&lt;/sub&gt;r&lt;sub&gt;p&lt;/sub&gt;, where d&lt;sub&gt;d&lt;/sub&gt; is MB size; d&lt;sub&gt;p&lt;/sub&gt; and r&lt;sub&gt;p&lt;/sub&gt; respectively are the size and the density of particles. Experiments performed on the copper ore flotation tailings at the Atalaya Mining (Spain) and Chaarat Kapan (Armenia) concentrators showed that, besides the optimal MB dose in the range of 1-3 ml/g, there is another optimal MB dose in the range of 10-20 ml/g, where the copper recovery increases by several percent compared to the reference test (f = 0). The deep minimum in copper recovery is observed in the area between the optimal MB doses, which is by several percent lower than the value in the reference test.

Soil Test Crop Response Concerning Soil Properties and Yield Attributes of Mustard (Brassica juncea L.) var. Krishna

We experimented in the field at the central research farm of the Department of Soil Science and Agricultural Chemistry, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj during Rabi season of 2022-23. The soil in the experimental area was characterized by a sandy loam texture. There were 27unit plots in total, with each plot sized at 2 X 2 m. We used a randomized block design, incorporating different levels of NPK (80:40:40), vermicompost, and two levels of sulfur (50% and 100%). The T9 (STCR+ 5t ha-1 Vermicompost+ 100% S) demonstrated that although there was a slight decrease in pH, bulk density, and particle density, there was a significant (P&lt;0.05) increase in pore space, water holding capacity, electrical conductivity (EC), organic carbon, and the availability of nitrogen, phosphorus, and potassium. Improvements were observed in plant growth and yield attributes, which showed the best results in terms of plant height, number of siliquae per plant, and total mustard yield compared to the control conditions. However, the use of organic manures and their combination with a full NPK treatment significantly enhanced the growth and overall yield attributes of mustard.