Perovskite nanocrystals (PNCs) have emerged as promising materials for photocatalytic applications due to their superior optoelectronic properties. Nevertheless, a detailed understanding of charge carrier separation and interfacial charge transfer remains dynamics are crucial for optimizing their photocatalytic and photovoltaic performance. In this work, the charge-transfer dynamics between MA0.5Cs0.5PbBr3 nanocrystals and 8-aminoqui-noline (AQ) were investigated using steady-state and time-resolved photoluminescence spectroscopy. Femtosecond photoluminescence upconversion measurements revealed ultrafast hot-carrier cooling with a rate 1.89 & times; 1012 s-1, indicating rapid relaxation of photogenerated carriers to the band edge. Energy-level analysis demonstrates that hole transfer from photoexcited PNCs to AQ is the dominant quenching pathway. The quenching mechanism involves two distinct processes: an ultrafast hot-hole transfer occurring on the picosecond timescale for AQ molecules located in the immediate vicinity of the nanocrystal surface, and a slower band-edge hole transfer mediated by diffusion-controlled interactions with AQ molecules distributed farther from the surface on the nanosecond timescale. The hot and band edge hole transfer rate, estimated from the time resolved PL measurements 1.23 & times; 1012 s-1 and 2.3 & times;1010 s-1 respectively. Furthermore, the photoluminescence quantum yield (PLQY) of the PNC-AQ system shows a strong dependence on excitation wavelength and quencher concentration. To predict the PLQY at any given excitation and AQ concentration, machine learning-based soft computing techniques, including fuzzy logic, artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS), were employed. Among these approaches, ANFIS provided the best predictive performance, closely reproducing the experimentally measured PLQY values.
Thermodynamic hydrate inhibitors (THIs) such as methanol and glycols are widely used in natural gas flow assurance but require high dosages and raise economic, safety, and environmental concerns. Kinetic hydrate inhibitors (KHIs) offer low-dosage alternatives by delaying hydrate nucleation and suppressing hydrate growth; however, many commercial KHIs suffer from limited biodegradability and moderate growth suppression performance. This study investigates iota-carrageenan (i-crgn), a renewable sulfated polysaccharide, as a low-dosage kinetic hydrate inhibitor and as a blend component for hydrate control. Methane hydrate inhibition experiments were conducted in a 500 cm3 high-pressure autoclave at 7.6 MPa using constant cooling (1 °C h⁻1) and isothermal (6 °C) protocols in methane–distilled water systems. Induction time (IT) and hydrate growth rate (HGR) were used as comparative performance metrics. I-crgn exhibited induction times comparable to poly(N-vinylpyrrolidone) (PVP) and poly(N-vinylcaprolactam) (PVCap), while consistently providing lower hydrate growth rates when used as a standalone inhibitor. When blended with commercial KHIs or selected solvents (4-methyl-1-pentanol, monoethylene glycol, and methanol), enhanced inhibition performance was observed in distilled water systems, manifested as delayed transition to rapid hydrate growth and reduced growth rates under identical conditions. Rheological measurements and selected hydrate tests were additionally performed in representative brine systems to assess solubility, gelation risk, and saline compatibility. At hydrate-relevant dosages (≈0.25 wt
Carbon capture, utilization, and storage (CCUS) is a critical strategy for mitigating anthropogenic carbon dioxide (CO2) emissions. One of the most feasible approaches under the CCUS framework is geological sequestration of CO2 in deep saline aquifers, depleted reservoirs, and coal bed seams. The storage of CO2 in saline aquifers occurs mainly through four trapping mechanisms, namely, structural, residual, solubility, and mineralization trapping, among which residual and solubility trapping play key roles. Predicting the effectiveness and security of geological carbon storage (GCS) requires an understanding of the interfacial behavior of CO2-brine-rock systems. The control of CO2 migration and its immobilization in subsurface formations is dictated by the solubility of CO2 in brine, the interfacial tension (IFT) between CO2 and brine, and the wettability of reservoir rocks. The integrated studies that consider these three parameters together are extremely limited. Hence, this work combines the CO2 solubility, IFT, and wettability of realistic shallow saline aquifer systems that have been investigated experimentally. An in-house experimental setup was used to carry out the CO2 solubility studies at different pressure, volume, and temperature equilibrium, and the calculations for solubility were done using the nonideal gas law. The captive bubble method and inverted pendant drop method were employed for wettability and IFT studies, respectively, in live water conditions, in a high-pressure, high-temperature goniometer setup. Experiments were conducted at three different pressures (30, 50, and 70 bar) and temperatures (40, 55, and 70 degrees C), in different brine compositions of NaCl and CaCl2 salts with four different ionic strengths ranging from 0 to 10 wt % (where 0 wt % corresponds to pure deionized water). Wettability studies were carried out on two pure mineral substrates (quartz and calcite). The studies showed an increase in solubility and CO2 wettability with increasing pressure, whereas the IFT between CO2 and brines decreased with an increase in pressure. The trends were reversed with an increase in the temperature, resulting in an increase in the IFT and a reduction in the CO2 solubility and wettability. The increase in brine salinity decreased the solubility, whereas the CO2 wettability of both the substrates and the IFT was found to increase with brine salinity. The increased dissolution of CO2 into the brine can enhance the solubility trapping capacity of saline aquifers. The reduction in the IFT between CO2 and the formation brine can increase the ability of gas to displace residual water from the pores. A maximum uncertainty (standard error) of 0.0837 mol/L, 0.5390 mN/m, 0.473 degrees, and 0.479 degrees was obtained for solubility, IFT, wettability on quartz, and calcite, respectively. The methods and results of these studies are expected to provide better insights into the initial screening of CO2 storage sites and the quantification of their trapping capacity in saline aquifers.
Developing persistent luminescence (PersL) materials that operate reliably at elevated temperatures is challenging due to rapid trap depopulation and thermal quenching of emission. Here, we demonstrate long-lasting PersL for several hours with stable multi-modal and multi-color emission at temperatures as high as 300 degrees C from Tb3+ in LaAlO3 host system after being co-doped with Eu3+ and Yb3+ for the first time. These LaAlO3:Tb3+,Eu3+,Yb3+ nanoparticles, synthesized via co-precipitation followed by a molten salt reaction, exhibit efficient downconversion, upconversion, PersL, optically stimulated luminescence (OSL), and thermally stimulated luminescence (TSL). TSL reveals four continuous trap levels, including a deep trap (similar to 290 degrees C) responsible for long-term charge storage and high-temperature stability. Notably, Tb3+ dopant acts not only as an emission center but also as an auxiliary trap for Eu3+ in the co-doped system. Density functional theory (DFT) calculations provide a unified trapping-de-trapping mechanism consistent with experimental results. These luminescent nanoparticles further exhibit strong radioluminescence with linear X-ray response, highlighting its potential as a scintillator, while its PersL further underscores its suitability for storage and imaging applications. These findings overcome the limitation of poor PersL in LaAlO3 and position our LaAlO3-based luminescent nanoparticles as desirable candidates for high-temperature optical data storage and anti-counterfeiting technologies.
This paper presents a robust and adaptive real-time control system for soft actuators based on ionic polymer-metal composites (IPMCs), where key challenges are in precision control due to their non-linear behavior, hysteresis, and environmental sensitivity. The proposed novel architecture consists of an artificial neural network (ANN) with a PID controller to minimize tip deflection error effectively. An experimental test setup has been developed to collect the deflection data of the IPMC actuator. A Baumer laser sensor captures accurate deflection data, which is digitized via a MAX485 module and processed using an Arduino microcontroller. In this study, a real-world dataset has been employed to train an ANN model in MATLAB software, which facilitates accurate prediction of IPMC deflection values corresponding to given voltage inputs. The control loop is implemented in Simulink, where a setpoint deflection feeds into a PID controller, which then adjusts the voltage input to the ANN model. The maximum steady-state error is up to 0.01 mm. This novel hybrid framework compensates for system non-linearities and sensor noise, significantly enhancing actuator performance. The integration of high-resolution sensing, intelligent modeling, and closed-loop control demonstrates an efficient solution for precise IPMC actuation in grippers, manipulation, soft robotics, bio-mimetic, and other applications.
