A Polyallylamine Anchored Amine‐Rich Laser‐Ablated Graphene Platform for Facile and Highly Selective Electrochemical IgG Biomarker Detection

Current immunosensors have an insufficient number of binding sites for the recognition of biomolecules, which leads to false positive or negative results. In this research, a facile, cost‐effective, disposable, and highly selective electrochemical immunosensing platform is developed based on cationic polyelectrolyte polyallylamine (PAAMI) anchored laser‐ablated graphene (LAG). Here, for the first time, PAAMI is introduced to stabilize LAG flakes, while retaining the intrinsic thermal and electronic properties of the substrate by noncovalent π–π interaction and electrostatic physical absorption. The sensing platform offers a suitable number of anchoring sites for the immobilized antibodies by providing ? NH₂ functional groups. The proper grafting of PAAMI is confirmed through X‐ray photoelectron spectroscopy and Raman spectroscopy. The immunosensing platform is applied to detect immunoglobulin (IgG) biomarkers as a proof of concept. Under optimized conditions, the sensing platform exhibits a linear range of 0.012–15 and 15–352 ng mL^?1 with a limit of detection of 6 pg mL^?1 for IgG detection with high selectivity. Based on the analysis, the developed immunosensing platform can be used for point‐of‐care detection of IgG in clinical diagnostic centers. Furthermore, the developed strategy is well suited for the detection of other cancer biomarkers after immobilizing the relevant antibodies.     

Composite‐Scattering Plasmonic Nanoprobes for Label‐Free,Quantitative Biomolecular Sensing

Biosensing based on localized surface plasmon resonance (LSPR) relies on concentrating light to a nanometeric spot and leads to a highly enhanced electromagnetic field near the metal nanostructure. Here, a design of plasmonic nanostructures based on rationally structured metal–dielectric combinations is presented, called composite scattering probes (CSPs), to generate an integrated multimodal biosensing platform featuring LSPR and surface‐enhanced Raman spectroscopy (SERS). Specifically, CSP configurations are proposed, which have several prominent resonance peaks enabling higher tunability and sensitivity for self‐referenced multiplexed analyte sensing. Using electron‐beam evaporation and thermal dewetting, large‐area, uniform, and tunable CSPs are fabricated, which are suitable for label‐free LSPR and SERS measurements. The CSP prototypes are used to demonstrate refractive index sensing and molecular analysis using albumin as a model analyte. By using partial least squares on recorded absorption profiles, differentiation of subtle changes in refractive index (as low as 0.001) in the CSP milieu is demonstrated. Additionally, CSPs facilitate complementary untargeted plasmon‐enhanced Raman measurements from the sample's compositional contributors. With further refinement, it is envisioned that the method may lead to a sensitive, versatile, and tunable platform for quantitative concentration determination and molecular fingerprinting, particularly where limited a priori information of the sample is available.     

Effect of Al Substitution in Structural and Magnetic Properties of MnCr<Subscript>2</Subscript>O<Subscript>4</Subscript>

Single-phase samples of Mn(Cr_1?x Al _x )₂O₄ (x = 0 – 0.30) with cubic spinel structure were prepared and the lattice constant is found to decrease from a = 8.4396 Å for x = 0 to a = 8.3801 Å for x = 0.30. The substitution of Al at Cr site is confirmed from the blue shift of Raman modes. Magnetization measurements and analysis show all the prepared samples exhibit ferrimagnetic transition with transition temperature in the range of 46 K for x = 0 to 33 K for x = 0.30. The saturation magnetization (M _s ) and the estimated anisotropy constant (K) show an anomalous behavior up to x = 0.10 and beyond that they decrease monotonously. They are explained by considering different site preferences of Al ³⁺ ions as the doping concentration is increased. The theoretical and experimental effective magnetic moment of the samples is found to be comparable and it decreases with increase in Al concentration.     

Highly dispersed platinum nanoparticles on graphitic carbon nitride: A highly active and durable electrocatalyst for oxidation of methanol,formic acid and formaldehyde

Finding efficient electrocatalyst for oxidation of small organic molecules such as methanol (CH₃OH), formic acid (HCOOH), formaldehyde (HCHO) etc. is essential for the development of their respective direct fuel cells. We report here highly dispersed platinum nanoparticles (PtNPs) on carbon nitride (CN_x) were successfully synthesized by the ultrasound mediated sodium borohydride reduction of H₂PtCl₆ in presence of CN_x nanosheets. This platinum–carbon nitride (Pt/CN_x) composite exhibited superior electrocatalytic activity towards oxidation of CH₃OH, HCOOH and HCHO in acid media. The mass activity, onset potential, tolerance to carbon monoxide (CO) poisoning and long term durability for the catalytic oxidation of CH₃OH, HCOOH, HCHO on Pt/CN_x catalyst in acid media is much higher than that of commercial Pt/C catalyst. The mass activity of Pt/CN_x catalyst at ～0.64 V (forward scan) is 310 mA/mg_Pt which is 2.7 time higher than that of commercial Pt/C for methanol oxidation. The electrooxidation of HCOOH on Pt/CN_x occurs via dual mechanism with greatly enhanced oxidation through dehydrogenation pathway in comparison with commercial Pt/C. The mass activity on Pt/CN_x at 0.3 V (vs. NHE) is 25 times higher than that of Pt/C for oxidation of HCOOH. The superior catalytic activity and durability of this Pt/CN_x catalyst can be attributed to high dispersion of PtNPs and strong catalyst support interaction.     

Electrical resistivity and magnetoresistivity of protonic acid (h2SO4 and HCl)‐doped polyaniline at low temperature

Electrical‐transport properties of protonic acid (H₂SO₄ and HCl)‐doped polyaniline (PANI) in an aqueous ethanol medium were investigated in the temperature range 1.8 K ≤ T ≤ 300 K and in a magnetic field up to 8 T. The room‐temperature resistivity of HCl‐doped PANI is larger than that of H₂SO₄‐doped PANI. The resistivity ratios ρ_r = ρ(1.8 K)/ρ(300 K) of the samples are high. The samples in the insulating region show a crossover from a Mott to an Efros–Shklovskii variable range hopping conduction at T = 9.8 K for H₂SO₄‐doped PANI and 8.5 K for HCl‐doped PANI. The magnetoresistivity of these samples is also explained by the variable range hopping theory. The different physical parameters were calculated from the experimental data. © 2000 John Wiley & Sons, Inc. J Appl Polym Sci 75: 1480–1486, 2000     

Studies on Double Diffusive Convection and Macro-segregation During Solidification of an Al-Alloy Based on Macro–Micro Model

In the present work, a study on the double diffusive convection and the macro-segregation during solidification of an Al-alloy (A356) is considered based on the macro?Cmicro model. The model considers a volume average single-domain approach to represent all the variables and properties as continuum in the entire domain where the transport phenomena during solidification are represented by mass, momentum, energy and species conservation equations. The evolution of solid during solidification, in the model, is predicted based on the microscopic phenomena, i.e. considering the nucleation and the growth of the nuclei. A semi-implicit finite volume method is adopted to discretize the governing equations and the discretized linear simultaneous equations are solved based on the SIMPLER and the TDMA algorithms. The simulation involves prediction of temperature, velocity and species in the computation domain during solidification of the alloy. A parametric study is also considered.     

Integrated system for the treatment of oxides of nitrogen from flue gases

A novel and effective system was developed for the complete treatment of NOx from flue gases. The system consisted of photocatalytic or ozone oxidation of NOx, followed by scrubbing and biological denitrification. Maximum photocatalytic oxidation of NOx was achieved while using powdered TiO2 at a catalytic loading rate of 10 g/h, relative humidity of 50%, and a space time of 10 s. The used catalyst was regenerated and reused. A total of 72% of oxidized NO was recovered as HNO3/HNO2 in the regeneration process. Stoichiometrically, 10% excess ozone was able to affect 100% oxidation of NO to NO2. Presence of SO2 adversely influenced the oxidation of NO by ozone. The scrubbing of NO was effective with distilled water. Heterotrophic denitrifiers were able to denitrify the leachate with an efficiency of 90%, using sewage (COD 450 mg/L) as electron donor. The new integrated treatment system seems to be a promising alternative for complete treatment of NOx from flue gases.     

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real-world applications, e.g., quality assurance and process monitoring. Specifically, variability in sample, system, and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a nonlinear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that the application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), because of its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data-highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples, as well as in related areas of forensic and biological sample analysis.     

Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose

Sample-to-sample variability has proven to be a major challenge in achieving calibration transfer in quantitative biological Raman spectroscopy. Multiple morphological and optical parameters, such as tissue absorption and scattering, physiological glucose dynamics and skin heterogeneity, vary significantly in a human population introducing nonanalyte specific features into the calibration model. In this paper, we show that fluctuations of such parameters in human subjects introduce curved (nonlinear) effects in the relationship between the concentrations of the analyte of interest and the mixture Raman spectra. To account for these curved effects, we propose the use of support vector machines (SVM) as a nonlinear regression method over conventional linear regression techniques such as partial least-squares (PLS). Using transcutaneous blood glucose detection as an example, we demonstrate that application of SVM enables a significant improvement (at least 30%) in cross-validation accuracy over PLS when measurements from multiple human volunteers are employed in the calibration set. Furthermore, using physical tissue models with randomized analyte concentrations and varying turbidities, we show that the fluctuations in turbidity alone causes curved effects which can only be adequately modeled using nonlinear regression techniques. The enhanced levels of accuracy obtained with the SVM based calibration models opens up avenues for prospective prediction in humans and thus for clinical translation of the technology.