A novel process to synthesize plasmonic MoO3–X nanosheets is demonstrated, in which MoS2 powders suspended in ethanol/water are irradiated with pulses from a femtosecond laser, resulting in simultaneous Coulomb explosion, photoexfoliation, and oxidation. The oxidation process is found to start with the formation of hydrogen-bonded molybdenum oxide (H X MoO3), followed by the release of −OH2 groups to create oxygen vacancies, and finally, MoO3–X is oxidized to MoO3 after extended irradiation. The formation of H X MoO3 is the critical step to create enough oxygen vacancies for localized surface plasmon resonance (LSPR), and this step is attributed to H3 + dissociated from ethanol under femtosecond laser irradiation. It is found that 80–90% ethanol is the optimal concentration to synthesize plasmonic MoO3–X , where the balance of water facilitates the release of the −OH2 groups to create the required vacancies. It is shown that different organic solvents like methanol, 1-propanol, and isopropyl alcohol that were reported to generate large amounts of H3 + under femtosecond laser irradiation can also oxidize MoS2 into plasmonic MoO3–X . The LSPR properties of the synthesized MoO3–X are evaluated by UV–vis spectroscopy and photothermal conversion measurements. A photothermal conversion efficiency of 33% is observed under near-infrared irradiation, suggesting a potential application in photothermal cancer therapy.

Drug delivery to the brain has been one of the toughest challenges researchers have faced to develop effective treatments for brain diseases. Owing to the blood–brain barrier (BBB), only a small portion of administered drug can reach the brain. A consequence of that is the need to administer a higher dose of the drug, which, expectedly, leads to a variety of unwanted side effects. Research in a variety of different fields has been underway for the past couple of decades to address this very serious and frequently lethal problem. One area of research that has produced optimistic results in recent years is nanomedicine. Nanomedicine is the science birthed by fusing the fields of nanotechnology, chemistry and medicine into one. Many different types of nanomedicine-based drug-delivery systems are currently being studied for the sole purpose of improved drug delivery to the brain. This review puts together and briefly summarizes some of the major breakthroughs in this crusade. Inorganic nanoparticle-based drug-delivery systems, such as gold nanoparticles and magnetic nanoparticles, are discussed, as well as some organic nanoparticulate systems. Amongst the organic drug-delivery nanosystems, polymeric micelles and dendrimers are discussed briefly and solid polymeric nanoparticles are explored in detail.

Virtual sensors, or soft sensors, have greatly contributed to the evolution of the sensing systems in industry. The soft sensors are process models having three fundamental categories, namely white-box (WB), black-box (BB) and gray-box (GB) models. WB models are based on process knowledge while the BB models are developed using data collected from the process. The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators. In this work, various design aspects of the GB models are discussed followed by their application in the process industry. In addition, the changes in the data-driven part of the GB models in the context of enormous amount of process data collected in Industry 4.0 are elaborated.

MoSe2 2H/1T hybrid nanoparticles are prepared by femtosecond laser ablation of MoSe2 powder in isopropyl alcohol with different laser powers and ablation times, and their formation mechanisms and photothermal conversion efficiencies (PTCEs) are studied. Two types of spherical nanoparticles are observed. The first type is onion‐structured nanoparticles that are formed by nucleation on the surfaces of melted droplets followed by inward growth of {002} planes of MoSe2. The second type is polycrystalline nanoparticles, formed by coalescence of crystalline nanoclusters fragmented from the powder during the laser ablation. The nanoparticle size in all samples shows a bimodal distribution, corresponding to different fragmentation mechanisms. The 2H‐to‐1T phase transition in the nanoparticles is likely caused by electron doping from the laser‐induced plasma. The PTCEs of the nanoparticles increase with laser power and ablation time; the highest PTCE is around 38%. After examining the bandgaps and the Urbach energies of the nanoparticles, it is found that the high PTCEs are primarily attributed to defects and structural disorder in the laser‐synthesized nanoparticles, which allow absorption of photons with energies smaller than the bandgap energy and facilitate non‐radiative recombination of photoexcited carriers.

Biodiesel production is a field of outstanding prospects due to the renewable nature of its feedstock and little to no overall CO 2 emissions to the environment. Data-based soft sensors are used in realizing stable and efficient operation of biodiesel production. However, the conventional data-based soft sensors cannot grasp the effect of process uncertainty on the process outcomes. In this study, a framework of data-based soft sensors was developed using ensemble learning method, i.e., boosting, for prediction of composition, quantity, and quality of product, i.e., fatty acid methyl esters (FAME), in biodiesel production process from vegetable oil. The ensemble learning method was integrated with the polynomial chaos expansion (PCE) method to quantify the effect of uncertainties in process variables on the target outcomes. The proposed modeling framework is highly accurate in prediction of the target outcomes and quantification of the effect of process uncertainty.

Entrained flow gasification is a commonly used method for conversion of coal into syngas. A stable and efficient operation of entrained flow coal gasification is always desired to reduce consumption of raw materials and utilities, and achieve higher productivity. However, uncertainty in the process hinders the stability and efficiency. In this work, a quantitative analysis of the effect of uncertainty on the conversion efficiency of the entrained flow gasification is performed. A data-driven, i.e., ensemble, model of the process was developed to predict conversion efficiency of the process. Then sensitivity analysis methods, i.e., Sobol and Fourier amplitude sensitivity test, were used to analyze the effect of each individual process variables on conversion efficiency. For analyzing the collective impact of uncertainty in process variables on conversion efficiency, a non-intrusive polynomial chaos expansion (PCE) method was used. The PCE predicts probability distribution of the conversion efficiency. Reliability of the process was determined on the basis of percentage of the probability distribution falling within control limits. Measured data is used to derive the control limits for off-line reliability analysis. For on-line reliability analysis of the process, measured data is not available so a just-in-time method, i.e., k–d tree, was used. The k–d tree searches the nearest neighbor sample from a database of historical data to determine the control limits.

Molybdenum blues (MBs) have attracted increasing attention due to their tunable structures and properties, which make them applicable in numerous fields. In this work, a novel strategy to synthesize MB nanorings by irradiating MoO3 suspended in water/ethanol mixtures with intense femtosecond laser pulses is demonstrated. It is found that the MoO3 can be dissolved in the water during laser irradiation to form molybdic acid, which provides an acidic environment for the formation of MB. Concentrations of ethanol as low as 1% result in the formation of MB and by adjusting the concentration of ethanol in the solvent, the absorption band can be tuned due to the modification of MoVOMoVI entities. The MB synthesized in 30% ethanol appears the darkest blue and assembly of vesicles 115 nm in size is observed. At high concentrations of ethanol (>70%), HXMoO3 and MoO3‐X are preferentially formed instead of MB. The photothermal conversion efficiency of the MBs synthesized in 1% and 30% ethanol is above 40%.

Introduction: Staphylococcus aureus is a chief source of both community and nosocomial infections. Isolates of Staphylococcal aureus from tertiary hospitals are resistant to frequently used antimicrobials. The intrinsically established methicillin-resistant S. aureus (MRSA) has been related with increased mortality and morbidity in hospital patients. Aim: This analysis was performed to determine the susceptibility of antibiotic pattern of staphylococcal aureus isolates with particular emphasis on methicillin-resistant S. aureus. Place and Duration: In the Department of Medicine and Pathology, Islam Medical and Dental College Sialkot for six-months duration from April 2021 to October 2021. Methods: Clinical samples from the Medicine ward were analysed and all isolates of S. aureus were involved in the study. Identification of isolates was done using a typical laboratory technique. The susceptibility antibiotic pattern of all strains of staphylococcal aureus was assessed using the improved Kirby Bauer method of antibiotic susceptibility. Results: Out of 120 isolates of S. aureus, MRSA were found to be in 29 (26.12%). The multidrug resistance percentage was 6.09% for MSSA and 75.86% for MRSA. All isolates of staphylococcal aureus were penicillin resistant. Though, sensitivity of all strains to vancomycin was noticed. Conclusions: This analysis exhibited an augmented incidence of MRSA in a Tertiary Care Hospital. Consistent investigation of nosocomial infections and susceptibility of antibiotics are essential to reduce the incidence of MRSA in hospitals and its spread in society. This study clearly demonstrates that the 1st line treatment for infection with MRSA is vancomycin. To maintain its worth, the usage of vancomycin must be restricted and only directed when clearly necessary. Keywords: MSSA, MRSA, Vancomycin, Antibiotic susceptibility and Staphylococcus aureus