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The aviation sector plays a vital role in global transportation, economic growth, and social integration. However, its rapid expansion has led to increased emissions. Sustainable aviation fuel (SAF) provides a promising solution by offering a clean-burning, renewable alternative to conventional jet fuel. SAF can be produced through various processes and feedstocks, significantly reducing the aviation industry’s environmental footprint. Fast pyrolysis (FP) presents a cost-effective and scalable approach for SAF production due to its low-cost feedstocks, rapid reaction times, and simpler technology. However, estimating the economic viability of FP for SAF production is complex and labor-intensive, requiring detailed process models and numerous assumptions. Furthermore, determining the relationship between feedstock properties and the minimum selling price (MSP) of the fuel can be challenging. To address these challenges, this study developed a data-driven framework for the preliminary estimation of SAF's MSP from FP. Synthetic data was generated using Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE), and hyperparameter optimization was performed using Grid Search to enhance model accuracy and predictions. Five surrogate models were evaluated: linear regression, gradient boost regression (GBR), random forest (RF), extreme boost regression (XGBoost), and elastic net. Among these, GBR and RF showed the most promise, based on metrics such as R², RMSE, and MAE for both original and synthetic datasets. Specifically, GBR achieved a Train R² of 0.9999 and a Test R² of 0.9277, while RF recorded Train and Test R² scores of 0.9789 and 0.9255, respectively. The use of data from the VAE further improved model accuracy. Additionally, a publicly accessible graphical user interface was developed, enabling researchers to estimate the MSP of SAF based on biomass properties, plant capacity, and location.

Quantum coherence, a fundamental aspect of quantum mechanics, plays a crucial role in various quantum information tasks. However, preserving coherence under extreme conditions, such as relativistic acceleration, poses significant challenges. In this paper, we investigate the influence of Unruh temperature and energy levels on the evolution of maximal steered coherence (MSC) for different initial states. Our results reveal that MSC is strongly dependent on Unruh temperature, exhibiting behaviors ranging from monotonic decline to non-monotonic recovery, depending on the initial state parameter (Delta _0). Notably, when (Delta _0=1), MSC is generated as Unruh temperature increases. Additionally, we observe that higher energy levels help preserve or enhance MSC in the presence of Unruh effects. These findings offer valuable insights into the intricate relationship between relativistic effects and quantum coherence, with potential applications in developing robust quantum technologies for non-inertial environments.

Observational data of the Earth’s weather and climate at the level of ground-based weather stations are prone to gaps due to a variety of causes. These gaps can inhibit scientific research as they impede the use of numerical models for agricultural, meteorological and climatological applications as well as introducing analytic biases. In this research, different machine learning techniques are evaluated together with traditional approaches to gap filling automated weather station data. When filling gaps for a specific data stream, data from neighbouring weather stations are used in addition to reanalysis data from the European Centre for Medium-Range Weather Forecasts atmospheric reanalyses of the global climate, ERA-5 Land. A novel gap creation method is introduced that provides 100% coverage in sampling the dataset while ensuring that the sampled data are randomly distributed. Gap filling across a range of different gap lengths and target variables are compared using a range of error functions. The variables selected for modelling are mean air temperature, dew point, mean relative humidity and leaf wetness. Our results show that models perform best on gap-filling temperature and dew point with worst performance on leaf wetness. As expected, model performance decreases with increasing gap length. Comparison between machine learning and reanalysis approaches show very promising results from a number of the machine learning models.

Remote analysis of the chemical composition of various substances in real time is a pressing problem. Current development of the fiber based evanescent wave spectroscopy (FEWS) in the mid-infrared range meets modern industrial, environmental, safety and medical needs. This paper presents a sensitivity analysis of the fiber probes for FEWS based on bent chalcogenide fibers as sensing elements. A previously developed methodology is applied to study the sensitivity of bent fiber tapers and bent fibers of constant diameter immersed in a liquid analyte. The wave optics based approach is used in calculations of the attenuation coefficients of the bend modes in the COMSOL Multiphysics solver. For evaluation of the bent fiber taper transmittance, the method of local modes is applied. The sensitivity analysis is carried out for the probes made of chalcogenide fibers of Ge₂₀Se₈₀ composition immersed in an aqueous solution of isopropyl alcohol with concentrations of 0 to 50 vol.%. The results of FEWS measurements with the U-shaped fiber taper of a certain geometry are used for assessment of the analysis outcomes. The role of light launching conditions in the operation of a fiber probe is discussed.

In this work, we explore the cosmological implications of three dynamic dark energy models-New Holographic Dark Energy (NHDE), Tsallis Holographic Dark Energy (THDE), and Barrow Holographic Dark Energy (BHDE)-within the framework of f(T) gravity. These models are motivated by the holographic principle and provide alternatives to the standard (Lambda )CDM model. We adopt a flat, isotropic Friedmann-Robertson-Walker (FRW) universe and employ a specific forms of f(T) gravity. The evolution of key cosmological parameters, such as the isotropic pressure, equation of state (EoS) parameter, and energy conditions, is analyzed for each model. Using observational data from Type Ia supernovae (SNIa), Cosmic Microwave Background (CMB), Baryon Acoustic Oscillations (BAO), and Hubble parameter measurements, we constrain the free parameters of each model and evaluate their compatibility with observational data. The analysis reveals that NHDE, THDE, and BHDE models are viable alternatives to (Lambda )CDM, offering a more dynamic description of dark energy. Each model satisfies key energy conditions, providing a stable framework for explaining cosmic acceleration. The results show deviations from the constant behavior of (Lambda )CDM, indicating the potential for time-evolving dark energy in these models.

The synergic effects of time dependence and thermodynamic driving on the metastable phase separation of liquid Fe₅₀Cu₅₀ hypoperitectic alloy were explored with three kinds of experimental methods including differential scanning calorimetry (DSC), laser heating, and drop tube. The calculated incubation time indicated that the secondary Cu-rich liquid phase kept the priority to nucleate when alloy undercooling exceeded 28 K. The cooling rates in three kinds of experiments covered six orders of magnitude from 3×10^–1 to 6×10⁵ K/s, and resulted in wide range of phase separation time and globule migration velocity. The extent of phase separation was determined by the globule migration distance in the phase separation time. Under 0.33 and 0.83 K/s slow cooling rates in DSC experiments, liquid phase separation was dominated by Stokes motion, and extended phase separation time led to more complete macrosegregation. At a higher cooling rate of 1500 K/s in laser heating experiment, the enhanced Marangoni migration resulted in distinctive macrosegregation in short phase separation time. Once liquid phase separation occurred under microgravity state in drop tube experiment, the phase separation time was the crucial factor dominating microstructure evolution. Core–shell macrosegregation formed in medium size alloy droplets with sufficient phase separation time, while dispersed structure appeared in small droplets with reduced phase separation time. Peritectic structure arose again due to the extremely short phase separation time in tiny alloy droplets.

Spontaneous scalarization in Einstein-scalar-Gauss–Bonnet theory admits both vacuum-general relativity (GR) and scalarized hairy black holes as valid solutions, which provides a distinctive signature of new physics in strong gravity regime. In this paper, we shall examine the optical features of Gauss–Bonnet black holes with spontaneous scalarization, which is governed by the coupling parameter (lambda ). We find that the photon sphere, critical impact parameter and innermost stable circular orbit all decrease as the increasing of (lambda ). Using observable data from Event Horizon Telescope, we establish the upper limit for (lambda ). Then we construct the optical appearances of the scalarized black holes illuminated by various thin accretions. Our findings reveal that the scalarized black holes consistently exhibit smaller shadow sizes and reduced brightness compared to Schwarzschild black holes. Notably, in the case of thin spherical accretion, the shadow of the scalarized black hole is smaller, but the surrounding bright ring is more pronounced. Our results highlight the observable features of the scalarized black holes, providing a distinguishable probe from their counterpart in GR in strong gravity regime.

The Large Hadron Collider’s high luminosity era presents major computational challenges in the analysis of collision events. Large amounts of Monte Carlo (MC) simulation will be required to constrain the statistical uncertainties of the simulated datasets below these of the experimental data. Modelling of high-energy particles propagating through the calorimeter section of the detector is the most computationally intensive MC simulation task. We introduce a technique combining recent advancements in generative models and quantum annealing for fast and efficient simulation of high-energy particle-calorimeter interactions.

A colorimetric sensor array is proposed for ultrasensitive detection and identification of bacteria by using Cl⁻ at various concentrations as sensing elements and triangular silver nanoparticles (T-AgNPs) as a single sensing nanoprobe. T-AgNPs are easily etched by Cl⁻. However, in the presence of bacteria, the etching process will be hindered. Different bacteria have differential protective effects on T-AgNPs due to their interactions, resulting in different etching degrees of T-AgNPs by Cl⁻, and visual color changes. By adjusting the antagonistic action between bacteria protection on T-AgNPs and the etching by using Cl⁻ at various concentrations, different bacteria had their own color response patterns. Combined with linear discriminant analysis (LDA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), the bacteria could be identified. The method was also used for bacteria mixtures identification and showed high sensitivity (OD₆₀₀ = 1.0 × 10⁻⁶) for V. parahaemolyticus detection. Finally, the sensor array was successfully utilized in the identification of bacteria in pure and mineral bottled water. The method is low-cost, simple, sensitive, visual, and has potential application in point-of-care testing of bacteria.

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Two modular systems were synthesized composed of triphenylamine (ZnTPAP) and pyrene (ZnPyP) covalently linked at meso position of the Zn(II) porphyrins. Both compounds behaved as energy transfer antenna and orthogonal units to enhance the electron donating ability of Zn(II) porphyrins. Detailed photophysical and aggregation studies reveal that an appreciable electronic interaction exists between peripheral units to the porphyrin π-system so that they behave like strong donor materials. The electrochemical and computational studies demonstrate delocalization of the frontier highest occupied molecular orbital (−5.08 eV) over the triphenylamine entities (ZnTPAP) in addition to the porphyrin macrocycle. Fluorescence experiments with ZnTPAP and ZnPyP in the presence of different nitro analytes at various concentrations show turn-off fluorescence behaviour and exhibit superior selectivity towards 2,4-dinitrophenol (DNP) with limit of detection (LOD) of ~ 2.3 and 9.2 ppm for ZnTPAP and ZnPyP. Photoinduced electron transfer process is involved in the static and dynamic fluorescence quenching process. A Stern–Volmer quenching association constant (K_sv) determination revealed that ZnTPAP is more sensitive than the ZnPyP. This is attributed to the strong donating behaviour of TPA units caused by intermolecular interaction through metal center and strong π–π interactions with nitro analytes. The present study provides new insights into the ability to tune the affinity and selectivity of porphyrin-based sensors utilising electronic factors associated with the central Zn(II) ion. Furthermore, a smartphone-interfaced portable fluorimetric method by recognising colour variations in RGB and the luminance (L) values facilitate sensitive and real-time sensing at low concentration levels will have a significant impact on development of a new class of chemosensors.

Graphical Abstract