Experimental validation of computational fluid dynamics modeling for narrow tillage tool draft

Energy requirement of a tillage tool, mostly represented by tool draft, is a function of different soil–tool interaction components like soil parameters, tool parameters and system parameters. Soil–tool interaction modeling was conducted using computational fluid dynamics (CFD) approach considering soil as a Bingham material. Soil bin tests were conducted to validate tool draft predictions obtained from this numerical modeling. Numerical predictions and soil bin experiments for the tool draft were observed with 40 mm wide vertical tool operating at four different depths of 40, 80, 120, and 160 mm. The tool was operated at four different operating speeds of 1, 8, 16 and 24 km h⁻¹ in clay loam soil with two moisture contents of 14% and 20%. Thus, the experimental design consisted in a (2 × 4 × 4) complete randomized factorial with two replications for each test. Simulation results over-predicted tool draft in comparison to the experimental values. The difference between the predicted and measured draft were not consistent and ranged from 1% to 42%, with an average of 24% and 22% for moisture contents of 14% and 20%, respectively. The agreement of simulation data with experimental values was higher at shallow depth of operation and lower tool operating speed. The correlation coefficient between the simulation and experimental draft were found to vary from 0.9275 to 0.9914.     

Benchmarking PES-Learn's machine learning models predicting accurate potential energy surface for quantum scattering

Machine learning (ML) models, neural networks, and Gaussian processes have been used to predict the potential energy surface taking C₂-He (both static and dynamic scenario) and NCCN-He collision systems. The surface is restricted to ∽125 points where traditional spline becomes inefficacious. Quantum dynamics is performed by solving close-coupling equation to compute cross sections benchmarking the performance of the ML models. The current study forms a basis for any future investigation of larger molecules where conventional fitting fails due to sparser ab initio points and cuts down the computational time without compromising on the quality of the surface.     

Correction to: Non‑precious transition metal‑based counter electrodes for dye‑sensitized solar cells

Journal of Solid State Electrochemistry -     

Nanohybrid Comprising Gold Nanoparticles – MoS2 Nanosheets for Electrochemical Sensing of Folic Acid in Serum Samples

Folic acid (FA) deficiency is associated with several clinical conditions such as megaloblastic anemia, neuropsychiatric, and pregnancy-related syndromes, this makes FA an important metabolite to be monitored. We have fabricated an electrochemical biosensor based on gold nanoparticles decorated molybdenum disulfide nanosheets (AuNPs−MoS₂NSs) nanocomposite as a transducer matrix for specific and rapid electrochemical detection of FA. Differential pulse voltammetry (DPV) studies displayed a rapid analytical response of the fabricated AuNPs−MoS₂NSs/GCE sensor probe towards FA in a wide concentration range of 0.001–100 μM with a very low detection limit of 0.72±0.03 nM. The selectivity of the fabricated sensor probe has been examined in the presence of interferents such as dopamine, uric acid, ascorbic acid, glucose, and urea. The clinical potential of the fabricated biosensor was established by monitoring FA in human serum samples. The developed AuNPs−MoS₂NSs/GCE sensor probe showed high reproducibility and stability, indicating its promise for FA detection in clinical settings.     

Non-precious transition metal-based counter electrodes for dye-sensitized solar cells

Journal of Solid State Electrochemistry - A non-platinum metal catalyst, TiNx-C, was synthesized through the high-pressure pyrolysis method, which was characterized using a transmission electron...