Search Results (144)

Polymer brushes (PBs) are transformative surface-modifying nanostructures, yet their synthesis via controlled methods like copper-mediated surface-initiated atom-transfer radical polymerization (Cu⁰-SI-ATRP) faces reproducibility challenges due to a lack of understanding of parameter interdependencies. This study systematically evaluates the Cu⁰-SI-ATRP process for polyacrylamide brushes (PAM-PBs), aiming to clarify key parameters that influence the synthesis process. This evaluation followed a step-by-step characterization that tracked molecular changes through infrared spectroscopy (IR) and surface development by contact angle (CA) through two different mixing methods: ultrasonic mixing and process simplification (Method A) and following literature-based parameters (Method B). Both methods, consisting of surface activation, 3-aminopropyltriethoxysilane (APTES) deposition, bromoisobutyryl bromide (BiBB) anchoring, and polymerization, were analyzed by varying parameters like concentration, temperature, and time. Results showed ultrasonication during surface activation enhanced siloxane (1139→1115 cm⁻¹) and amine (1531 cm⁻¹) group availability while reducing APTES concentration to 1 Vol% without drying sufficed for BiBB anchoring. BiBB exhibited insensitivity to concentration but benefited from premixing, evidenced by sharp C–Br (~1170 cm⁻¹) and methyl (3000–2800 cm⁻¹) bands. Additionally, it was observed that PAM-PBs improved with Method A, which had reduced variance in polymer fingerprint regions compared to Method B. Adding to the above, CA measurements gave complementary step-by-step information along the modifications of the surface, revealing distinct wettability behaviors between bulk PAM and synthesized PAM-PBs (from 51° to 37°). As such, this work identifies key parameter influence (e.g., mixing, BiBB concentration), simplifies steps (drying omission, lower APTES concentration), and demonstrates a step-by-step, systematic parameter decoupling that reduces variability. In essence, this detailed parameter analysis addresses the PAM-PBs synthesis process with better reproducibility than the previously reported synthesis method and achieves the identification of characteristic behaviors across the step-by-step process without the imperative need for higher-cost characterizations. Full article

Papilloscopy, the science of human identification through fingerprints, has seen notable advancements in developing less toxic latent fingerprint developers (LFDs), especially from natural feedstock. Tannins, the second most abundant natural polyphenol, present a potential eco-friendly and cost-effective alternative, with no record of their use as LFDs in the existing literature. This study characterized four types of tannins from black wattle, using Fourier Transform Infrared Spectroscopy, revealing key functional groups like C=O, C=C, and O–H. Ultraviolet–visible absorption spectra showed similar behaviors for all tannins, indicating phenolic and benzenoid structures. Energy-dispersive X-ray Spectroscopy identified high concentrations of chlorine, sodium, potassium, and sulfur, naturally found in biomass and soil. Finally, elements in significant concentrations, such as sodium, potassium, iron, zinc, and copper, were found through the incineration of the spent bark. On the basis of these findings, the tannin with the highest potential for LFD was selected. Combining this tannin with spent bark ash resulted in a composite whose performance was evaluated using different methods, including depletion studies, tests with various donors, and assessments on different surfaces. The results demonstrated that this combination significantly enhanced the material’s efficiency by integrating organic and inorganic properties, which improved visual contrast and powder adhesion. Full article

Natural deep eutectic solvents (NADESs) have emerged as a promising eco-friendly alternative to petrochemicals for extracting plant metabolites. Considering that the demand for sustainable “green” ingredients for industrial applications is growing, those solvents are purported to develop extracts with interesting phytochemical fingerprints and biological activities. Given the interest in flavonoids from Chenopodium quinoa Willd. leaves, an efficient “green” extraction method was developed by investigating eight NADESs with defined molar ratios, i.e., malic acid-choline chloride (chcl)-water (w) (1:1:2, N1), chcl-glucose-w (5:2:5, N2), proline-malic acid-w (1:1:3, N3), glucose-fructose-sucrose-w (1:1:1:11, N4), 1,2-propanediol-chcl-w (1:1:1, N5), lactic acid-glucose-w (5:1:3, N6), glycerol-chcl-w (2:1:1, N7), and xylitol-chcl-w (1:2:3, N8). Rheological measurements of all NADESs confirmed their pseudoplastic behaviors. To improve the extraction processes, differential scanning calorimetry (DSC) allowed us to determine the maximum amount of water that could be added to the most stable NADES (N1, N2, N3, and N4; 17.5%, 20%, 10%, and 10% w/w, respectively) to lower their viscosities without disturbing their eutectic environments. The phytochemical compositions of NADES extracts were analyzed using high-performance thin-layer chromatography (HPTLC), and their free radical scavenging and α-amylase inhibitory properties were assessed using HPTLC-bioautography. N2, diluted with 20% of water, and N7 presented the best potential for replacing methanol for an eco-friendly extraction of flavonoids, radical scavengers, and α-amylase inhibitors from quinoa leaves. Their biological properties, combined with a good understanding of both thermal behavior and viscosity, make the obtained quinoa leaf NADES extracts good candidates for direct incorporation in nutraceutical formulations. Full article

We present a comprehensive structural and thermomechanical investigation of N-salicylideneaniline, a Schiff base derivative that exhibits remarkable thermosalient phase transition behavior. By combining variable-temperature X-ray powder diffraction (VT-XRPD), differential scanning calorimetry (DSC), hot-stage microscopy, and Hirshfeld surface analysis, we reveal two distinct thermosalient mechanisms operating in different polymorphic forms. Form I displays pronounced anisotropic thermal expansion with negative strain along a principal axis, culminating in a sudden and explosive phase transition into Form IV. In contrast, Form III transforms more gradually through a microstrain accumulation mechanism. Fingerprint plots and contact evolution from Hirshfeld surface analysis further support this dual-mechanism model. These insights highlight the importance of integrating macro- and microscale structural descriptors to fully capture the mechanical behavior of responsive molecular solids. The findings not only enhance the fundamental understanding of thermosalience but also inform the rational design of functional materials for actuating and sensing applications. Full article

Cryptocurrency such as Bitcoin supports anonymous routing (Tor and I2P) due to the application requirements of anonymity and censorship resistance. In permissionless and open networking for cryptocurrency, an adversary can spoof to pretend to use Tor or I2P for anonymity and privacy protection, while, in reality, it is not using anonymous routing and is forwarding its networking directly to the destination peer to reduce networking overheads. Using profile detection based on deterministic features to detect anonymous routing and false claims is vulnerable to spoofing, especially in permissionless cryptocurrency bypassing registration control. We thus designed and built a method of network fingerprinting, using networking behaviors to detect and classify networking types. We built a network sensor to collect data on an active Bitcoin node connected to the Mainnet and applied supervised machine learning to identify whether a peer node was using IP (direct forwarding without the relays for anonymity protection), Tor, or I2P. Our results show that our scheme is effective in accurately detecting networking types and identifying spoofing attempts through supervised machine learning. We tested our scheme using multiple supervised learning models, specifically CatBoost, Random Forest, and HistGradientBoosting. CatBoost and Random Forest performed best and had comparable accuracy performance in effectively detecting false claims, i.e., they classified the networking types and detected fake claims of Tor usage with 93% accuracy and false claims of I2P with 94% accuracy in permissionless Bitcoin. However, CatBoost-based detection was significantly quicker than Random Forest and HistGradientBoosting in real-time testing and detection. Full article

Integrating digital twins (DTs) into smart grid systems within the Internet of Smart Grid Things (IoSGT) ecosystem brings novel opportunities but also security challenges. Specifically, advanced machine learning (ML)-based Deepfake technologies enable adversaries to create highly realistic yet fraudulent DTs, threatening critical infrastructures’ reliability, safety, and integrity. In this paper, we introduce Authenticating Networked Computerized Handling of Representations for Smart Grid security (ANCHOR-Grid), an innovative authentication framework that leverages Electric Network Frequency (ENF) signals as real-world anchors to secure smart grid DTs at the frontier against Deepfake attacks. By capturing distinctive ENF variations from physical grid components and embedding these environmental fingerprints into their digital counterparts, ANCHOR-Grid provides a robust mechanism to ensure the authenticity and trustworthiness of virtual representations. We conducted comprehensive simulations and experiments within a virtual smart grid environment to evaluate ANCHOR-Grid. We crafted both authentic and Deepfake DTs of grid components, with the latter attempting to mimic legitimate behavior but lacking correct ENF signatures. Our results show that ANCHOR-Grid effectively differentiates between authentic and fraudulent DTs, demonstrating its potential as a reliable security layer for smart grid systems operating in the IoSGT ecosystem. In our virtual smart grid simulations, ANCHOR-Grid achieved a detection rate of 99.8% with only 0.2% false positives for Deepfake DTs at a sparse attack rate (1 forged packet per 500 legitimate packets). At a higher attack frequency (1 forged packet per 50 legitimate packets), it maintained a robust 97.5% detection rate with 1.5% false positives. Against replay attacks, it detected 94% of 5 s-old signatures and 98.5% of 120 s-old signatures. Even with 5% injected noise, detection remained at 96.5% (dropping to 88% at 20% noise), and under network latencies from <5 ms to 200 ms, accuracy ranged from 99.9% down to 95%. These results demonstrate ANCHOR-Grid’s high reliability and practical viability for securing smart grid DTs. These findings highlight the importance of integrating real-world environmental data into authentication processes for critical infrastructure and lay the foundation for future research on leveraging physical world cues to secure digital ecosystems. Full article

Detecting behavioral routines is an important research area with many implications in various practical applications. One such application involves studying the behavior of older adults residing in care homes. This paper proposes a comprehensive methodology for extracting and analyzing the daily routines of older adults in care homes. The methodology utilizes raw data comprising signal strength measurements obtained from smartwatches worn by six volunteers over five months. To establish the basis for estimating daily activities, fingerprint-based localization techniques are employed to track the minute-by-minute location of each volunteer. Subsequently, the activity performed by each volunteer is estimated for each day. Finally, the study estimates the probability of a user undertaking each one of the studied activities on a given weekday. Full article

In this work, we studied the thermal behavior and infrared fingerprint of anhydrous and hydrated DL-tartaric acid via conventional and modulated Differential Scanning Calorimetry (DSC), Thermogravimetry (TGA), Fourier Transform Infrared Spectroscopy (FTIR), nuclear magnetic resonance (NMR), pH measurements, and ab initio density functional theory (DFT) calculations. Six samples were examined in total (raw, recrystallized from D₂O solution, freeze-dried, and three heated samples). The results reveal that both forms (anhydrous and hydrated) do not exhibit melting prior to decomposition. It is also shown that the so-called DL-tartaric acid does not exist in the solid state in pure form, but it contains water and a tartaric acid oligomer, which is produced through esterification. Alteration of the chemical structure (reflected through decomposition) is initiated at quite low temperatures and is more pronounced for the hydrated form. Up to 75 °C, decomposition proceeds through esterification, while at higher temperatures it seems to be reversed due to the increase in water and decrease in COOH groups emerging through anhydride formation. Either upon heating or at sub-zero temperatures during freeze-drying, the hydrated form decomposes, and although some water is removed, new water is produced due to esterification. The conclusions are also supported by DFT calculations. Full article

The liver is the most common site of metastasis of colorectal cancer (CRC), and colorectal liver metastasis is one of the major causes of CRC-related deaths worldwide. The tumor microenvironment, particularly the extracellular matrix (ECM), plays a critical role in CRC metastasis and chemoresistance. Based on findings from clinical and basic research, this review attempts to offer a complete understanding of the role of the ECM in colorectal liver metastasis and to suggest potential ways for therapeutic intervention. First, the ECMs’ role in regulating cancer cell fate is explored. We then discuss the hepatic ECM fingerprint and its influence on the metastatic behavior of CRC cells, highlighting key molecular interactions that promote metastasis. In addition, we examine how changes in the ECM within the metastatic niche contribute to chemoresistance, focusing on ECM remodeling by ECM stiffening and the activation of specific signaling pathways. Understanding these mechanisms is crucial for the development of novel strategies to overcome metastasis and improve outcomes for CRC patients. Full article

Background: Detecting intracellular diffusion dynamics with high spatiotemporal resolution is critical for understanding the complex molecular mechanisms that govern viral infection, drug delivery, and sustained receptor signaling within cellular compartments. Although considerable progress has been made, accurately distinguishing between different types of diffusion in three dimensions remains a significant challenge. Methods: This study extends a previously established two-dimensional, machine learning-based diffusional fingerprinting approach into a three-dimensional framework to overcome this limitation. It presents an algorithm that predicts intracellular motion types based on a comprehensive feature set, including custom statistical descriptors and standard Imaris-derived trajectory features, which capture subtle variations in individual trajectories. The approach employs an extended gradient-boosted decision trees classifier trained on an array of synthetic trajectories designed to simulate diffusion behaviors typical of intracellular environments. Results: The machine learning classifier demonstrated a classification accuracy of over 90% on synthetic datasets, effectively capturing and distinguishing complex diffusion patterns. Subsequent validation using an experimental dataset confirmed the robustness of the approach. The incorporation of the Imaris track features streamlined diffusion classification and enhanced adaptability across diverse volumetric imaging modalities. Conclusions: This work advances our ability to classify intracellular diffusion dynamics in three dimensions and provides a method that is well-suited for high-resolution analysis of intracellular receptor trafficking, intracellular transport of pathogenic agents, and drug delivery mechanisms. Full article

Smart terminals use passwords and physiological characteristics such as fingerprints to authenticate users. Traditional authentication methods work when users unlock their phones, but they cannot continuously verify the user’s legal identity. Therefore, the one-time authentication implemented by conventional authentication methods cannot meet security requirements. Implicit authentication technology based on user behavior characteristics is proposed to achieve the continuous and uninterrupted authentication of savvy terminal users. This paper proposes an implicit authentication method that fuses keystroke and sensor data. To improve the accuracy of authentication, a neural network-based feature extraction model that integrates keystroke data and motion sensor data is designed. A feature space with dual-channel fusion is constructed, and a dataset collected in real scenarios is built by considering the changes in user activity scenarios and the differences in terminal holding postures. Experimental results on the collected data show that the proposed method has improved the accuracy of user authentication to a certain extent. Full article

Background/Objectives: Tumor interactions with their surrounding environment, particularly in the case of peritumoral edema, play a significant role in tumor behavior and progression. While most studies focus on the radiomic features of the tumor core, this work investigates whether peritumoral edema exhibits distinct radiomic fingerprints specific to glioma (GLI), meningioma (MEN), and metastasis (MET). By analyzing these patterns, we aim to deepen our understanding of the tumor microenvironment’s role in tumor development and progression. Methods: Radiomic features were extracted from peritumoral edema regions in T1-weighted (T1), post-gadolinium T1-weighted (T1-c), T2-weighted (T2), and T2 Fluid-Attenuated Inversion Recovery (T2-FLAIR) sequences. Three classification tasks using those features were then conducted: differentiating between Low-Grade Glioma (LGG) and High-Grade Glioma (HGG), distinguishing GLI from MET and MEN, and examining all four tumor types, i.e., LGG, HGG, MET, and MEN, to observe how tumor-specific signatures manifest in peritumoral edema. Model performance was assessed using balanced accuracy derived from 10-fold cross-validation. Results: The radiomic fingerprints specific to tumor types were more distinct in the peritumoral regions of T1-c images compared to other modalities. The best models, utilizing all features extracted from the peritumoral regions of T1-c images, achieved balanced accuracies of 0.86, 0.81, and 0.76 for the LGG-HGG, GLI-MET-MEN, and LGG-HGG-MET-MEN tasks, respectively. Conclusions: This study demonstrates that peritumoral edema, as characterized by radiomic features extracted from MRIs, contains fingerprints specific to tumor type, providing a non-invasive approach to understanding tumor-brain interactions. The results of this study hold the potential for predicting recurrence, distinguishing progression from pseudo-progression, and assessing treatment-induced changes, particularly in gliomas. Full article

This paper focuses on differentiating between ideal and non-ideal chemical systems based on their kinetic behavior within a closed isothermal chemical environment. Non-ideality is examined using the non-ideal Marcelin–de Donde model. The analysis primarily addresses ‘soft’ non-ideality, where the equilibrium composition for a reversible non-ideal chemical system is identical to the corresponding composition for the ideal chemical system. Our approach in distinguishing the ideal and non-ideal systems is based on the properties of the special event, i.e., event, the time of which is well-defined. For the single-step first-order reaction in the ideal system, this event is the half-time-decay point, or the intersection point. For the two consecutive reversible reactions in the ideal system, A ↔ B ↔ C, this event is the extremum obtained within the conservatively perturbed equilibrium (CPE) procedure. For the non-ideal correspondent models, the times of chosen events significantly depend on the initial concentrations. The obtained difference in the behavior of the times of these events (intersection point and CPE-extremum point) between the ideal and non-ideal systems is proposed as the kinetic fingerprint for distinguishing these systems. Full article

The NLRP3 inflammasome, plays a critical role in the pathogenesis of rheumatoid arthritis (RA) by activating inflammatory cytokines such as IL1β and IL18. Targeting NLRP3 has emerged as a promising therapeutic strategy for RA. In this study, a multidisciplinary approach combining machine learning, quantitative structure–activity relationship (QSAR) modeling, structure–activity landscape index (SALI), docking, molecular dynamics (MD), and molecular mechanics Poisson–Boltzmann surface area MM/PBSA assays was employed to identify novel NLRP3 inhibitors. The ChEMBL database was used to retrieve compounds with known IC₅₀ values to train machine learning (ML) models using the Lazy Predict package. After data pre-processing, 401 non-redundant structures were selected for exploratory data analysis (EDA). PubChem and MACCS fingerprints were used to predict the inhibitory activities of the compounds. SALI was used to identify structurally similar compounds with significantly different biological activities. The compounds were docked using MOE to assess their binding affinities and interactions with key residues in NLRP3. The models were evaluated, and a comparative analysis revealed that the ensemble Random Forest (RF) model (PubChem fingerprints) with RMSE (0.731), R² (0.622), and MAPE (8.988) and bootstrap aggregating model (MACCS fingerprints) with RMSE (0.687), R² (0.666), and MAPE (9.216) on the testing set performed well, in accordance with the Organization for Economic Cooperation and Development (OECD) guidelines. Out of all docked compounds, the two most promising compounds (ChEMBL5289544 and ChEMBL5219789) with binding scores of −7.5 and −8.2 kcal/mol were further investigated by MD to evaluate their stability and dynamic behavior within the binding site. MD simulations (200 ns) revealed strong structural stability, flexibility, and interactions in the selected complexes. MM/PBSA binding free energy calculations revealed that van der Waals and electrostatic forces were the key drivers of the binding of the protein with ligands. The outcomes obtained can be used to design more potent and selective NLRP3 inhibitors as therapeutic agents for the treatment of inflammatory diseases such as RA. However, concerns related to the lack of large datasets, experimental validation, and high computational costs remain. Full article

Light element alloying in iron is required to explain density deficit and seismic wave velocities in Earth’s core. However, the light element composition of the Earth’s core seems hard to constrain as nearly all light element alloying would reduce the density and sound velocity (elastic moduli). The alloying light elements include oxidizing elements like oxygen and sulfur and reducing elements like hydrogen and carbon, yet their chemical effects in the alloy system are less discussed. Moreover, Fe-X-ray Absorption Near Edge Structure (Fe-XANES) fingerprints have been studied for silicate materials with ferrous and ferric ions, while not many X-ray absorption spectroscopy (XAS) studies have focused on iron alloys, especially at high pressures. To investigate the bonding nature of iron alloys in planetary interiors, we presented X-ray absorption spectroscopy of iron–nitrogen and iron–carbon alloys at high pressures up to 50 GPa. Together with existing literature on iron–carbon, –hydrogen alloys, we analyzed their edge positions and found no significant difference in the degree of oxidation among these alloys. Pressure effects on edge positions were also found negligible. Our theoretical simulation of the valence state of iron, alloyed with S, C, O, N, and P also showed nearly unchanged behavior under pressures up to 300 GPa. This finding indicates that the high pressure bonding of iron alloyed with light elements closely resembles bonding at the ambient conditions. We suggest that the chemical properties of light elements constrain which ones can coexist within iron alloys. Full article