Temperature Shift Research Articles

Al Islands on Si(111): Growth Temperature, Morphology and Strain

The comprehensive structural studies of thin island Al films with a thickness of 20–50 nm deposited by magnetron sputtering on Si(111) substrates in an argon plasma at a pressure of 6*10–3 mbar and a temperature from 20 to 500°C are presented. Studies of the morphology and microstructure of the films were carried out using XRD, SEM, EDS and TEM methods. It has been found that most of the islands are Al {001} and Al {111} crystallites with lateral sizes of 10–100 nm, differently conjugated with Si(111) substrate. At room temperature of the substrate, only Al {001} crystallites are epitaxially formed on it. The Al {111} crystallites epitaxially grown on the substrate dominate as the substrate temperature increases about 400°C. The influence of the temperature of the Si(111) substrate on the process of epitaxial growth of crystallites, the dynamics of their shape and structural perfection is shown. It has been found that crystallites epitaxially connected to the substrate experience deformation ε = 7 × 10–3 and ε = –2 × 10–3 for Al {001} and Al {111}, respectively. It has been shown that for thin island Al films on Si(111), the dependence of the number of crystallization centers and the particle growth rate on the supercooling temperature is consistent with the band model of crystallization. At the same time, a shift in the characteristic temperatures for the zone boundaries is observed due to the properties of the substrate. This must be taken into account when engineering the surface morphology and structural perfection of crystallites in Al island magnetron films.

Impact of hydrothermal aging on microstructure transformation in Poly(L‐lactide) nonwovens under tension in condition close to a living organism

AbstractThe analysis of mechanical properties and structure of bioresorbable polymer nonwoven materials is an important area of research in the medical industry, the properties and structure of which directly affect the processes of cellular activity. In this work, the processes of reorganization of the fibrous microstructure of poly(l‐lactide) nonwoven materials under uniaxial tension in a water environment were investigated. The study which included volumetric ultrasound imaging, mechanical testing, differential scanning calorimetry, X‐ray diffraction, and melting rate measurements was the first attempt to identify correlations between the mechanical behavior of fibrous meshes and changes in the supramolecular structure of the polymer during 3 months of hydrothermal aging T = 37°C. An increase in crystallinity by 4%, a shift of glass transition temperature by 4°C, and a 2 times increase in melt flow rate under hydrolysis were indicated degradation of the amorphous phase. Local degradation of the amorphous phase of fibers led to the formation of surface cracks, an increase in the number of microcracks during hydrothermal aging resulted in a decrease in the mobility of fibers in the volume of the nonwoven material and a decrease in the elasticity of the entire nonwoven material, which was revealed using the volume ultrasound imaging and optical microphotographs.

Impact of statistical poly(propylene co-ethylene) on the thermo-mechanical properties of high-density polyethylene

A series of high-density polyethylene and a statistical copolymer of poly(propylene-co-ethylene) blends in a wide range, namely (0, 10, 20, 30, 40, 50, 60,70, 80, 90, 100) abbreviated as HDPE/VM were systematically investigated by using a rheometer, differential scanning calorimetry (DSC) measurements, polarized optical microscopy (POM) and tensile tests. Rheometer results show that adding VM decreases dynamic viscosity, storage, and loss modulus. Han plot shows that HDPE and VM are compatible and miscible in the range from 20 to 60 VM % in the molten state. DSC results show little nucleation effect of VM on HDPE (HDPE’s melt crystallization temperature shifts 2 °C higher). Moreover, a linear composition dependence of ∆cp ∆Hc, ∆Hm shows that PE and VM are most probably compatible in the molten state in composition range from 20 to 60%. However, upon crystallization, the VM and PE domains occur distinctively. The results of the tensile tests demonstrated a decrease in elastic modulus, yield stress, and ultimate tensile strength as VM content increased. At low VM content (less than 20%), high elongation at break was detected for the blends, and very fine spherulites of HDPE were found across the sample by POM.

Unveiling the Monoclinic Phase in CsPbBr3-xClx Perovskite Crystals, Phase Transition Suppression and High Energy Resolution γ-Ray Detection.

All-inorganic CsPbBr3 and CsPbCl3 perovskites are promising materials for high-performance solar cells and advanced radiation detection technologies with high stability. Here we report that CsPbBr3-xClx (x = 0-3) crystals exhibit eutectoid behavior for the melting points and phase transition temperatures. The well-known halide perovskite cubic phase transition temperature shifts near room temperature (∼37 °C for CsPbBr2Cl). We conducted an extensive crystallographic analysis on single crystals of 7 different compositions, including the end members CsPbBr3 and CsPbCl3. Contrary to previous beliefs, we discovered they exhibit a monoclinic structure with space group symmetry P21/m at room temperature, rather than the orthorhombic Pnma. This new structural model is more precise and features a unit cell volume that is four times larger than that of the orthorhombic model. From high-quality single crystals of CsPbBr2Cl, grown by the Bridgman method, we constructed γ-ray detectors achieving an energy resolution of 7.2% at 200 V for 57Co radiation. Thermally stimulated current spectroscopy of the CsPbBr2Cl samples revealed that the defect densities in crystals from different regions of the ingot were relatively uniform, with values of ∼4.72 × 1012 and ∼5.09 × 1012 cm-3. These findings indicate that low deep-level defect densities can be achieved that are consistent with the notable performance of the CsPbBr2Cl perovskite as a high-energy γ radiation detector.

White Light Generation and Stability Analysis of High-Power Blue LDs with Remote YAG Phosphors

This paper presents a comprehensive analysis of white light generation and the associated aging dynamics using high-power blue laser diodes (LDs) combined with transmissive single crystal remote YAG phosphors. By systematically varying input currents (ranging from 0.6 A to 3 A) and phosphor thicknesses (250 μm and 500 μm), this study elucidates the optical and electrical characteristics of LD-phosphor systems under diverse operating conditions. The results highlight the system’s potential for stable and efficient white light generation, making it suitable for high-power lighting applications. Experimental setups included both single LDs and a 4 × 2 LD array. For the single LD, a peak optical output of 4.16 W was achieved at 3 A, corresponding to an initial luminous flux of approximately 700 Lm and a correlated color temperature (CCT) of 4653 K, with minimal color temperature shift observed during a 60 min aging process. The 4 × 2 LD array demonstrated consistent white light output across varying phosphor thicknesses, with maximum luminous fluxes of 1857 Lm at 1.4 A and 2622 Lm at 1.6 A for phosphor thicknesses of 250 μm and 500 μm, respectively. Importantly, the phosphor exhibited excellent thermal stability throughout the aging process, with the CCT maintained within a range of 4600 K to 5500 K. These findings underscore the reliability and applicability of LD-based white light systems in demanding high-power lighting environments, offering a promising alternative to conventional light sources for automotive, industrial, and specialized lighting applications.

Rapid Detection of SLCO1B1 Polymorphisms Using Duplex Fluorescence Melting Curve Analysis: Implications for Personalized Drug Dosing in Clinical Settings.

The polymorphism of the solute carrier organic anion transporter family member 1B1 (SLCO1B1) gene exerts a marked influence on drug transport, thus playing a pivotal role in personalized drug dosing. This study endeavours to establish a rapid, precise, and straightforward method for detecting SLCO1B1 genetic variants utilizing Duplex Fluorescence Melting Curve Analysis (DFMCA). Whole blood samples were collected from 54 individuals from Meizhou People's Hospital (2023.01-2023.03), with a mean age of 58.90 years (SD = 7.86), including 28 men and 26 women. DNA was extracted from these samples and subjected to PCR amplification targeting two allelic regions. Primers, fluorescent probes, and corresponding allelic target sequences were designed specifically for two common SLCO1B1 polymorphisms (rs2306283 and rs4149056). The functionality of the fluorescent probes in binding to their respective allelic targets was verified using melting curve analysis, enabling the identification of distinct melting temperatures for different genotypes. Subsequently, DFMCA was employed to differentiate genotypes based on the melting temperature shifts of the corresponding fluorescent probes. The sensitivity, accuracy, and consistency of the method were evaluated, with sequencing validation performed on a subset of samples. DFMCA facilitated the concurrent detection and accurate genotyping of both polymorphisms within 2hours, demonstrating concordance with sequencing results from randomly selected samples. Importantly, stable detection performance was achieved for human genomic DNA at concentrations ≥ 3.125 ng. In a cohort comprising Han Chinese individuals from southern China, the allele frequencies for rs2306283 (A: 28.7%, G: 71.3%) and rs4149056 (T: 88.89%, C: 11.11%) concurred well with previous studies in the Han Chinese population. The SNP typing system utilizing DFMCA technology presents advantages in terms of speed, ease of use, accuracy, and cost-effectiveness, making it a suitable tool.

Restricted Dispersal and Phenotypic Response to Water Depth in a Foundation Seagrass.

Species conservation and management benefit from precise understanding of natural patterns of dispersal and genetic variation. Using recent advances in indirect genetic methods applied to both adult plants and dispersed seeds, we find that the mean seed dispersal in a threatened marine foundation plant (the eelgrass Zostera marina) is approximately 100-200 m. This distance is surprisingly more similar to that of wind-dispersed terrestrial seeds (~10s to 100s of meters) than the passive dispersal of marine propagules via currents (~10s to 100s of kilometres). Because nearshore marine plants like Zostera are commonly distributed across strong selective gradients driven by bathymetry (depth) even within these restricted spatial scales, seeds are capable of dispersing to novel water depths and experiencing profound shifts in light availability, temperature and wave exposure. We documented strong phenotypic variation and genome-wide differentiation among plants separated by approximately the spatial scale of mean realised dispersal. This result suggests genetic isolation by environment in response to depth-related environmental gradients as one plausible explanation for this pattern. The ratio of effective to census size (or Ne/Nc) approximated 0.1%, indicating that a fraction of existing plants provides the genetic variation to allow adaptation to environmental change. Our results suggest that successful conservation of seagrass meadows that can adapt to microspatial and temporal variation in environmental conditions will be low without direct and persistent intervention using large numbers of individuals or a targeted selection of genotypes.

Characteristics of Climate Change in Poyang Lake Basin and Its Impact on Net Primary Productivity

Climate change exerts substantial impacts on human society and the carbon cycle of terrestrial ecosystems. Studying the spatiotemporal characteristics of regional climate change and its impact on carbon sequestration is an important topic in ecology and environmental science. This study utilized meteorological and land use/cover data to explore these dynamics. Statistical methods such as the Mann–Kendall (M-K) test and wavelet analysis were used to simulate the changes in annual average temperature and precipitation in the Poyang Lake Basin from 1980 to 2020. The Carnegie–Ames–Stanford Approach (CASA) model was used to estimate the interannual variation in net primary productivity (NPP) in the region over the past 40 years. Additionally, the present study examined the influence of various factors on NPP changes. The main results are as follows: (1) Over the past four decades, the average temperature in the Poyang Lake Basin was 17.85 °C, while the average precipitation was 1621.35 mm. The average annual temperature rises at a rate of 0.27 °C per decade. (2) A significant shift in the average annual temperature occurred in the early 21st century, and annual precipitation exhibited multiple abrupt changes during the mid-to-late 1990s. Both temperature and precipitation changes follow a 25-year cycle, with temperature hotspots located in the south and precipitation hotspots in the northeast. (3) The impact of climate change on the change in NPP in the Poyang Lake Basin is about 70%, with the annual average temperature having a significant effect on the increase in NPP. This study can provide a scientific foundation for formulating policies aimed at mitigating climate-related disasters and enhancing carbon cycling in terrestrial ecosystems.

Short-term effects of ambient temperature variability on myocardial infarction hospitalizations in Sweden: A nationwide case-crossover study

Abstract Background Climate change threatens human health and general welfare via multiple dimensions. However, the short-term effect of temperature variability, a crucial aspect of climate change, on myocardial infarction (MI) remains largely unexplored. Purpose We aimed to assess the short-term effects of temperature variability on MI hospitalizations within the nationwide SWEDEHEART registry. Methods This population-based nationwide study involved 228,897 MI patients from the SWEDEHEART registry in Sweden from 2005 to 2019. High-resolution (1 km × 1 km) daily mean ambient temperature was estimated using a spatiotemporal machine learning methodology and assigned to patients’ home addresses. Temperature variability was calculated as the difference between the same-day (as the MI event) ambient temperature and the average temperature recorded over the preceding seven days. Specifically, an upward temperature shift represents a rise in the current day’s temperature relative to the seven-day average, while a downward temperature shift indicates a corresponding decrease. A time-stratified case-crossover design incorporating a conditional logistic regression model with distributed lag non-linear model was applied to estimate the association between ambient temperature variability and total MI, ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI). Moreover, we assessed potential effect modification by sex and medication intake. The results were expressed as odds ratio (OR, with their 95% confidence intervals [CIs]) per 1°C increase in temperature variability. Results Our study found that a 1°C increase in upward temperature shift was significantly associated with increased risks for total MI, STEMI, and NSTEMI (OR [95% CI]: 1.010 [1.006-1.015], 1.014 [1.006-1.022], and 1.009 [1.004-1.014], respectively). Furthermore, a 1°C greater in downward temperature shift was significantly associated with decreased risks for total MI and NSTEMI (OR [95% CI]: 0.995 [0.991-0.999], and 0.993 [0.988-0.998], respectively). Moreover, we found that males and patients who were on any heart medication, particularly digitalis, were at a higher risk of MI associated with exposure to higher upward temperature shifts compared to females and their counterparts not taking these medications. Conclusions This nationwide case-crossover study provides novel evidence that short-term exposure to increasing upward temperature shift is associated with an increased risk of MI hospitalization. Our finding highlights the cardiovascular health threats posed by upward temperature shifts, which are anticipated to increase in frequency and intensity due to climate change.

Quantitative detection of refrigerant charge faults in multi-unit air conditioning systems based on machine learning algorithms

Refrigerant charging discrepancies constitute the predominant malfunctions in air conditioning systems. Achieving the optimal charging level is crucial for system performance, underscoring the importance of precise refrigerant level prediction. This study introduces an algorithm designed for the quantitative detection of refrigerant charging errors by integrating the Markov Transition Field (MTF), Convolutional Neural Networks (CNN), and Multi-head Self-Attention (MSA) mechanisms. A high-precision enthalpy difference chamber was employed to establish a Variable Refrigerant Flow (VRF) refrigerant charging test bench. This setup facilitated the analysis of system parameter sensitivity to charging faults and aided in the creation of a training dataset for the algorithm. Comparative analysis was conducted against Support Vector Machines (SVM), Random Forests (RF), CNN with Self-Attention (AT), and MTF-CNN-MSA. The findings reveal that our method adeptly captures temporal dependencies and dynamic shifts in time series as visual representations, offering novel insights for discerning fault patterns within such data. Notably, the maximum pressure variations at high-pressure and low-pressure points were 0.25 MPa and 0.07 MPa, respectively, with temperature shifts of 12 °C and 3.5 °C at the high and low-temperature points. The high-pressure and high-temperature points are particularly sensitive to changes in refrigerant charging, and parameters from these sections were utilized to construct the dataset. The CNN-MSA algorithm demonstrates consistent performance across various fault types, effectively delineating fault characteristics. The accuracies achieved by SVM, RF, CNN-AT, and MTF-CNN-MSA were 84.38 %, 73.75 %, 88.13 %, and 93.75 %, respectively. In comparison, the CNN-MSA algorithm was able to more accurately detect refrigerant charge faults at different levels.

Study on enhancing mechanical and thermal properties of carbon fiber reinforced epoxy composite through zinc oxide nanofiller

This study investigates the enhancement of mechanical and thermal properties of carbon fiber reinforced epoxy composites by incorporating zinc oxide (ZnO) nanofillers. The composites were developed by adding 3–15 g of ZnO nanoparticles into the epoxy matrix and were evaluated through experimental testing. The results showed significant improvements in mechanical performance, with the maximum tensile strength of 112.49 MPa and flexural strength of 114.82 MPa at the 15 g ZnO concentration. SEM analysis revealed a reduction in void content and improved fiber-matrix adhesion, enhancing load transfer. Thermal conductivity is 13.47 W/mK, while the coefficient of linear thermal expansion is 9.29 × 10−5/°C, indicating superior thermal stability. TGA analysis demonstrated a shift in decomposition temperature from 310 to 375 °C, confirming enhanced thermal stability. These results highlight the positive influence of ZnO nanofillers on both the mechanical and thermal properties of carbon fiber reinforced epoxy composites, making them more suitable for advanced structural applications.

Catalytic enhancement of water gas shift reaction with Cu/ZnO/ZSM-5: Overcoming challenges of CO2 and H2 rich feeds

Water gas shift (WGS) reaction commonly converts CO to CO2 using H2O and produces H2 and CO2 in a catalytic fixed bed reactor. This study aims to develop a Cu/ZnO/ZSM-5 catalyst loaded in water gas shift reactor for converting typical synthesis gas produced from dry reforming of methane (DRM) at medium temperature shift (MTS, 200–350 oC) conditions and for preventing reverse reaction due to high feed concentrations of CO2 and H2. The Cu/ZnO/ZSM-5 catalyst was prepared by impregnation method with lower Cu metal loading of 5, 10, and 15 wt%, respectively, compared to commercial catalysts. The synthesized catalyst was characterized using XRF, XRD, SEM, H2-TPR, NH3-TPD, and TGA. The catalyst activity and stability for the WGS reaction were tested in the fixed bed reactor at atmospheric pressure and temperature of 325 °C. The experimental results show that CO conversion increases with increasing Cu loading. The 15 wt% Cu/ZnO/ZSM-5 catalyst showed the best results with a CO conversion of 35% and a yield of H2 of 36% and showed good stability for 32 h. Based on the TGA, the 15 wt% Cu/ZnO/ZSM-5 catalyst forms a 0.04 g carbon/g catalyst, which is much lower when compared to commercial catalyst (0.105 g carbon/g catalyst). The relative activity of the synthesized catalyst indicates 2.4 times better when compared to commercial catalysts. High concentrations of H2 and CO2 in the feed may initiate side reactions, including reverse WGS, methanation, and carbon formation, which will decrease H2 yield.

The impact of technological advancements on global warming

Global warming, or climate change, refers to long term shifts in temperature and weather patterns. These shifts may be natural, however, from 1870, which is the start of the technical or second industrial revolution, there has been abnormally large amount of carbon dioxide (CO2) and other greenhouse gases such as methane and nitrous oxide getting trapped in the atmosphere, resulting in an enhanced greenhouse effect causing global temperatures to increase. This study seeks to assess and analyse the impact technology, its advancements and how it has impacted the global temperatures. By examining the correlation between technological development and CO2 emissions from machinery, this study will provide a comprehensive analysis of the relationship between the two.

Understanding the implications of climate change for Australia’s surface water resources: Challenges and future directions

Climate change is altering Australia’s streamflow and water resources due to a complex interplay of shifts in temperature, rainfall, evaporation, vegetation dynamics, soil moisture, and rainfall-runoff partitioning. Meanwhile increases in human activity and urbanisation continue to alter the landscape, further affecting streamflow and flood volumes. Understanding the nature of these recent changes is critical for evaluating future risks and ensuring planning decisions are sustainable. Positively, our knowledge of changes to Australia’s hydrologic regimes has greatly advanced in the last decade. Here we review our current understanding of historical hydrologic changes, focusing first on the drivers of change and then observed changes in streamflow and flooding, before turning our attention to the role that historical observations can play in anticipating future climate change impacts. We conclude with recommendations to help better understand and plan for an unknown future.Increases in extreme rainfalls have increased the risk of large flood events. However, declines in mean rainfall across large parts of the continent, increases in diversions, and increasing evaporate demand have led to an overall drying. Significant advances in our understanding have been made through analysing trends in the observational record and investigating the associated drivers. While these learnings may not reliably inform the potential effects of future change, they can still inform process understanding ultimately helping accurately model and project future impacts. Projecting climate change impacts on water resources in Australia remains challenging, and progress will require improved instrumentation to observe and understand catchment responses together with methods and management techniques that incorporate this additional uncertainty. We argue it is through process-informed models together with multi-scale observational data that this gap can be breached.

Climate Change Effects on Spatiotemporal Distribution of Precipitation over West Central India: A Statistical Downscaling Approach

The long-term shifts in temperatures and weather patterns are referred to as climate change. Climate change not only leads to long-term shifts in average temperatures but also changes in the spatial and temporal distribution of rainfall over continents. This study aims to predict likely changes in the rainfall pattern induced by climate change over the West Central (WC) region of India. The approach uses a statistical downscaling technique that converts coarse-scale outputs of the global climate model (GCM) to high-resolution future precipitation projections giving refined distribution. The decision support tool that uses a robust statistical downscaling technique viz. statistical downscaling model (SDSM) is used for assessing local climate change impacts. The research includes the study of likely regional climate variability, by integrating historical observational data and empirical relationships between large-scale climate variables and local weather patterns. Historical data and the SDSM, version 4.2, are employed to forecast future rainfall trends. Rainfall data from the India Meteorological Department and the National Centre for Environmental Prediction (NCEP) from 1961 to 2001 are used along with outputs from general circulation models (GCMs), viz. Hadley Centre coupled model, version 3 (HadCM3), and coupled global climate model, version 3 (CGCM3), for the period 1961–2099. Rainfall scenarios are presented for three future time periods (2011–2040, 2041–2070, and 2071–2099). The study indicates a significant increase in the mean annual precipitation across the West Central India region, particularly in the 2050s and 2080s. Mean annual rainfall is projected to rise by 10–19.4% under HadCM3 A2 and B2 scenarios. The HadCM3 indicates the month of September as the month of the highest precipitation in later time periods, whereas it is the month of August, according to CGCM3 simulations. When comparing the results of the two models, HadCM3 gives better results, as indicated by better R2 value in validation. Thus, the analysis gives climate change-induced likely changes in the spatiotemporal distribution of precipitation over the West Central India region. The insight given by the work will be useful for decision making in many sectors like agriculture, water management, disaster risk reduction, and infrastructure planning.

Understanding the local implications of climate change: Unpacking the experiences of smallholder farmers in Thulamela Municipality, Vhembe District, Limpopo Province, South Africa

Climate change is experienced locally. However, climate change impact assessments are often done at the international, regional and national levels. Local level impacts are less prolific. When international, regional and national level predictions are applied to the local level, they are out of context. Therefore, it is important to understand the local impact of climate change to enhance formulation of suitable adaptation strategies. This study aimed to understand the local impacts of climate change on smallholder farmers in Thulamela Municipality, Vhembe District, Limpopo Province, South Africa. The actual experiences of smallholder farmers were unpacked. Face-to-face interviews with farmers in the region were conducted to solicit data on the climatic changes experienced, the impacts on crop and livestock production and social wellbeing. Data on the intensity of the impacts was also solicited. Results show that the experienced climatic changes emanated from temperature changes and shifts in rainfall patterns. The impacts on crop and livestock production as well as social wellbeing are all negative. Farmers indicated that the impacts were mostly average to high. It is concluded that smallholder farmers are aware that climate change impacts are negatively affecting their livelihoods. Investments towards building the capacity of smallholder farmers are pertinent.

Genetically engineered synthetic cells activate cargo release upon temperature shift.

Genetically engineered synthetic cells activate cargo release upon temperature shift.

Thermal performance analysis of supporting column ultra-thin vapor chamber with graded microgroove

To analyze the thermal behavior of a graded microgroove ultra-thin vapor chamber with supporting column, a three-dimensional steady-state numerical model coupled with graded capillary force network, which involves hydraulic and heat transfer on hydraulic and heat transfer performance on ultra-thin vapor chamber with different supporting column sizes. This model considers the influence of supporting column with varying diameters and intervals on liquid flow and temperature distribution, as well as the effects of a graded capillary force network on heat transfer limit of ultra-thin vapor chamber. The numerical results show that the additional heat transfer provided by the central support column causes an outward shift in the saturation temperature of vapor, creating a new liquid reflux path at the condensation surface. The accuracy of the model is verified by the preparation of ultra-thin vapor chamber with a graded microgroove a graded microgroove. Compared with the experimental results on conventional microgrooves, graded microgrooves can improve the heat transfer limit by 29.3%. According to the theoretical analysis model, the graded capillary force network can effectively resist incremental increases in vapor pressure drop, enhancing the efficiency of gas–liquid circulation of ultra-thin vapor chamber.

Fluorescence labeling-based differential scanning fluorimetry, an effective method for protein thermal stability and protein-compound binding analysis

Differential scanning fluorimetry (DSF) is widely used to assess protein thermal stability and protein-ligand interaction. However, its utility is often limited by the presence of detergents, which can affect hydrophobic binding. To tackle this issue, we developed an effective fluorescence-labeled DSF (FL-DSF) technique that tracks protein denaturation by monitoring the labeling fluorescence decrease, thus overcoming challenges typically encountered with traditional DSF methods. In this research, FL-DSF was first validated using Peroxisome Proliferators-Activated Receptor γ (PPARγ), Retinoid X Receptor α (RXRα), and Lysozyme, confirming its accuracy in determining melting curves. Expectedly, FL-DSF also exhibited strong compatibility with detergents in our investigations. Besides this, a new calculation method was proposed to characterize the protein denaturation process and evaluate protein-ligand binding. This mathematical model goes beyond traditional approaches, which simply treated the melting temperature (TM) shift as a concentration-dependent variable. Instead, it comprehensively incorporates the influence of irreversible denaturation-induced native protein loss on the equilibrium of protein-ligand binding. This methodology was successfully applied into the evaluation of binding affinity for 2 classical binding systems of PPARγ-Rosiglitazone and RXRα-CD3254. It was also utilized for the following binding screening studies, leading to the discovery of promising ligands for PPARγ, RXRα, and Lysozyme.

Unveiling the magnetic structure of BaFeO3-y: Shedding light on the elusive magnetic behavior

This work provides a comprehensive examination of the structural and magnetic properties of 6H-BaFeO2.96 over a wide temperature range (10 K < T < 300 K) using neutron and synchrotron X-ray diffraction. Additionally, the local oxidation state of iron is examined using electron energy loss spectroscopy. No structural changes that could indicate a charge-order transition are observed in spite of a reported possible charge disproportionation of Fe4+ into Fe(4+δ)+ and Fe(4-δ)+ phenomenon at low temperature. According to the magnetic characterization, BaFeO2.96 orders antiferromagnetically at TN=156 K. The application of external magnetic fields strongly influences the magnetic behavior; the transition temperature shifts to lower values with the applied magnetic field and the long-range magnetic order melts with an applied field of 14 T. The magnetocaloric effect clearly shows at TN a change from negative to positive magnetic entropy. The Rietveld refinement of the neutron powder diffraction data collected at 10 K, gives a magnetic ordering with propagation vector [0, 0, ½] and three magnetically distinct sites for the Fe atoms. This magnetic structure consists of ferromagnetic Fe-sheets perpendicular to the c-axis with the magnetic moments along the [110] direction coupled both ferromagnetic and antiferromagnetically along the c-axis. The magnetic moment values of the three Fe ions are very different evidencing a delicate equilibrium of competing magnetic interactions.