Corrosion Behaviour of Metals in Artificial Tear Solution

Human tear comes in contact with a number of instruments during operation in the eyes.  This results in a variety of undesirable effects such as corrosion and malfunction.  Corrosion behaviour of five metals, namely, mild steel (MS), mild steel coated with zinc (MS-Zn), Ni-Cr, Ni-Ti super elastic (Ni-Ti.SE), and SS 316 L in artificial tear solution has been studied by polarization study and AC impedance spectra.  The study reveals that the decreasing order of corrosion resistance in artificial tear solution is : Ni-Ti SE > Ni-Cr > SS 316 L > MS-Zn > MS.  The first three metals are better candidates and the first one is the best candidate for making instruments used in operation in the eyes, in presence of tears

Study on etiological and clinical profile of acute symptomatic seizures in adults in a tertiary care hospital.

OBJECTIVES: 1. To study the etiological profile of acute symptomatic seizures in various age groups. 2. To assess the common seizure type in patients with acute symptomatic seizures of varied etiologies. 3. To study the Electro Encephalographic and Radiological profile of Acute symptomatic seizures. DESIGN: Single Observational study. This study was conducted among 150 Acute symptomatic seizure patients who were admitted in our hospital with various etiologies since March 2013 to February 2014. RESULTS: In the present study Acute symptomatic seizures were slightly more in males than in females. Acute symptomatic seizures were most common in 40 – 60 years of age group. Generalised seizures were the most common seizure type encountered in the study. Head ache, vomiting and altered sensorium were the most common non convulsive presenting symptoms. Diabetes and Hypertension were the co morbid systemic illness associated with cerebrovascular accidents. Metabolic abnormality was found as the cause in 13% of the patients and the predominant age group was above 60 years. Cerebrovascular diseases were the most frequent etiology in acute symptomatic seizures. Among the cerebrovascular diseases, cortical venous thrombosis and intra cerebral haemorrhage were commonly presented with seizure. Next common cause of acute symptomatic seizures in this study was CNS infections consisting of 19% of the patients. Among CNS infection, Neurocysticercosis was the most common cause. In females eclampsia and cortical venous thrombosis were the common etiology for acute symptomatic seizures. Alcohol related seizures contributed to etiology in 12% of the patients. Hanging and post cardiac arrest were the common etiology the anoxic brain injury. Generalised seizures were common with cerebrovascular disease and alcohol related seizures. Partial seizures were common with Tuberculoma and Neurocysticercosis. Myoclonic seizures were common with anoxic brain injury. Cerebrovascular disease and Metabolic abnormality were common above 60 years where as eclampsia and alcohol related seizures were common in 20 – 60 years of age. Acute CNS infection was the predominant cause of seizures below 20 years of age. Conclusion: Acute symptomatic seizures were more common in males than females and in 40 – 60 years of age. Cerebrovascular diseases were the most frequent cause of acute symptomatic seizures, followed by Acute CNS infections. Eclampsia and cortical venous thrombosis were the common etiology among females. Cerebrovascular diseases and metabolic abnormality were common above 60 years of age where as eclampsia and alcohol related seizures were common in 20-60 years of age. Acute CNS infections were the predominant cause of acute symptomatic seizures below 20 years of age. Generalised seizures were the most common seizure type encountered in this study. EEG and Radiological abnormalities were seen in nearly 60% of the patients

Heavy Metal Bioaccumulation in Sediment and Benthic Biota

Bioaccumulation can be used as a measurement tool for analyses of sediment and soil toxicity. Heavy metal toxicity in sediments can be measured with bioaccumulation tests. Metal bioaccumulation has recently achieved more concentration from researchers due to its feasibility to conduct both field and laboratory experiments with indicative organisms. Bioaccumulation can be measured directly or using models. For this study, the concentrations of trace metals (Zn, Pb and Cu) in earthworm tissues were analyzed and compared with the total contents of heavy metals in contaminated parts of soils of Pallikaranai marshland. Samples were taken from different parts of the marshland, which have been reported to have heavy metal presence decades ago. Mostly predominant species found in the marshland L. mauritii and P. excavatus were used for the experiment. Soil samples were collected at six points along a gradient of increasing pollution. A regression model was applied to the results, and the order of accumulation of heavy metals BAF in the present study is Zn > Cu > Pb, indicating that zinc is a potentially high accumulating metal compared to Cu and Pb

Composition and Structure Based GGA Bandgap Prediction Using Machine Learning Approach

This study focuses on developing precise machine learning (ML) regression models for predicting energy bandgap values based on chemical compositions and crystal structures. The primary aim is to match the accuracy of predictions derived from GGA-PBE calculations and validate them through density functional theory (DFT)-based band structure calculations. We assessed eight standalone ML regression models, including AdaBoost, Bagging, CatBoost, LGBM, RF, DT, GB, and XGB. These models were analyzed for their ability to predict GGA-PBE bandgap values across diverse material structures and compositions, using a dataset containing bandgap values for 106,113 compounds. Additionally, we constructed four ensemble models using the stacking method and seven using the bagging method. These ensemble models incorporated RidgeCV and LassoCV to explore if ensemble techniques could enhance prediction accuracy. The dataset was divided into subsets of varying sizes: 10,000, 25,000, 50,000, and 100,000 entries. We determined feature importance through permutation techniques and established a correlation coefficient matrix using the Pearson correlation method. The Random Forest (RF) model emerged as the top performer among standalone models, achieving an R2 value of 0.943 and an RMSE value of 0.504 eV. Bagging regression demonstrated improved performance across different dataset sizes with streamlined feature selection. Ensemble models, particularly bagging, consistently outperformed standalone models, achieving the best R2 value of 0.948 and an RMSE value of 0.479 eV in the test dataset. Using the best-performing model, we predicted bandgap values for new half-Heusler compounds with 18 valence electron counts. These predictions were successfully validated using accurate DFT calculations. DFT calculations indicated that the newly predicted compounds are narrow bandgap semiconductors with dynamic stability.Comment: 17 pages, 17 figures, Research pape

Accelerating Discovery of Vacancy Ordered 18-Valence Electron Half-Heusler Compounds: A Synergistic Approach of Machine Learning and Density Functional Theory

In this study, we attempted to model vacancy ordered half Heusler compounds with 18 valence electron count (VHH) derived from 19 VEC compounds such as TiNiSb such that the compositions will be Ti0.75NiSb, Zr0.75NiSb and Hf0.75NiSb with semiconducting behavior. The main motivation is that such a vacancy-ordered phase not only introduces semi conductivity but also it disrupts the phonon conducting path in HH alloys and thus reduces the thermal conductivity and as a consequence enhances the thermoelectric figure of merit. In order to predict the formation energy ({\Delta}Hf) from composition and crystal structure we have used 4684 compounds for their {\Delta}Hf values are available in the material project database and trained a machine learning model with R2 value of 0.943. Using this trained model, we have predicted the {\Delta}Hf of a list of VHH. From the predicted database of VHH we have selected Zr0.75NiSb and Hf0.75NiSb to validate the machine learning prediction using accurate DFT calculation. The calculated {\Delta}Hf for these two compounds from DFT calculation are found to be comparable with our ML prediction. The calculated electronic and lattice dynamics properties show that these materials are narrow band gap semiconductors and are dynamically stable as their all-phonon dispersion curves are having positive frequencies. The calculated Seebeck coefficient, electrical conductivity as well as thermal conductivity, power factor and thermoelectric figure of merit are analyzed.Comment: 5 pages, 2 figures, conferenc

1′-Methyl-4′-phenyl-2′′-sulfanylidene­dispiro­[indoline-3,2′-pyrrolidine-3′,5′′-1,3-thia­zolidine]-2,4′′-dione

The title compound, C20H17N3O2S2, crystallizes with two mol­ecules in the asymmetric unit. The pyrrolo­dine rings have envelope conformations in both mol­ecules, the N atoms deviating by 0.574 (3) and 0.612 (2) Å from the mean planes through the other ring atoms. The 1′-methyl and 4′-phenyl groups on the pyrrolidine rings are substituted in equatorial positions. In the crystal, mol­ecules are linked into a three-dimensional network by N—H⋯O, N—H⋯N and C—H⋯O and N—H⋯π hydrogen bonds

Methyl 1-ethyl-3′-[hydroxy(naphthalen-1-yl)methyl]-1′-methyl-2- oxospiro[indoline-3,2′-pyrrolidine]-3′-carboxylate

In the title compound, C27H28N2O4, the pyrrolidine ring adopts a twist conformation. The plane of the indole ring is almost perpendicular to that of the pyrrolidine ring, making a dihedral angle of 88.50 (6)°. The planes of the naphthyl ring system and the pyrrolidine ring are tilted by an angle of 55.86 (5)°. The molecular conformation is stabilized by intramolecular O—H...O and O—H...N hydrogen bonds

3,4-Dibromo-2,5-dimethyl-1-phenyl­sulfonyl-1H-pyrrole

In the title compound, C12H11Br2NO2S, the dihedral angle between the two rings is 78.79 (12)°. The crystal packing features C—H⋯π inter­actions

3-(4-Methoxy­phen­yl)-6-(phenyl­sulfon­yl)perhydro-1,3-thiazolo[3′,4′:1,2]pyrrolo[4,5-c]pyrrole

In the title compound, C21H24N2O3S2, the three five-membered rings adopt envelope conformations. The dihedral angle between the two aromatic rings is 68.4 (1)°. C—H⋯O inter­actions link the mol­ecules into a chain and the chains are cross-linked via C—H⋯π inter­actions involving the meth­oxy­phenyl ring