Features Of Spectra Research Articles

Micro-explosion-induced Combustion and Agglomeration Characteristics in Composite Propellants with Fluorinated Graphene

The potential of the Al-F reaction in suppressing agglomeration during propellant combustion and enhancing combustion performance is investigated by introducing fluorinated graphene as a fluorinated oxidizer. Comparative analyses of ignition combustion and agglomeration behaviors are conducted on novel composite powders and propellant samples modified with varying contents of fluorinated graphene using laser and hot wire ignition visualization systems. Characterizing parameters such as characteristic spectra, flame grayscale, ignition delay time, combustion duration, and burning rate are measured during combustion at different pressures. Additionally, agglomerated particles are collected via quenching techniques under 7 MPa pressure to explore the influence mechanism of fluorinated graphene on agglomeration near the burning surface, and a comprehensive influence mechanism is proposed. Results indicate that fluorinated graphene promotes ammonium perchlorate decomposition, accelerates oxidizing gas release, and enhances thermal conduction at the burning surface. The reaction between Al and F decreases the formation of intermediates (AlO and Al2O), while the interaction of F with Al and Al2O3 effectively inhibits the clustering of Al particles, replacing conventional oxidation reactions and resulting in a unique micro-explosion jetting phenomenon. The introduction of 15% fluorinated graphene concentrates most product particles around 10 μm, enhancing energy release during combustion. Overall, this composite powder containing fluorinated graphene effectively improves the combustion performance of aluminum-containing composite propellants, inhibiting Al particle agglomeration and potentially reducing specific impulse loss in solid rocket motors.

Effect of temperature and frequency on the dielectric behaviour of cellulose nanocrystals

Nanocellulose is a promising material for advanced applications due to its renewable nature, non-toxicity, eco-friendliness, mechanical strength and other unique properties. This study investigates the dielectric properties of cellulose nanocrystals (CNCs) derived from wastepaper through acid hydrolysis, across a temperature range of 30°C to 100°C and a frequency range of 1 Hz to 10 MHz. Using theoretical models such as the modified Cole-Cole model, Jonscher's universal dielectric response (UDR) model and modified Kohlrausch-Williams-Watts (KWW) model, a detailed analysis is performed. The results indicate that the dielectric properties depend on temperature, with the frequency dispersion of the real (ε′) and imaginary parts (ε″) of the dielectric permittivity fitting the modified Cole-Cole equation. These values show an initial increase up to 70°C (ε′∼ 6000 and dielectric loss <1) followed by a decrease at higher temperatures, which is linked to the hydroxyl groups. A temperature dependence is also observed in space charge conductivity, free charge conductivity and relaxation time. Non-Debye type behaviour is attributed to asymmetrical features in the electric modulus spectra, as explained by the modified KWW model at various temperatures. The frequency variation of AC conductivity adheres to Jonscher’s power law and the temperature variation of the frequency exponent (0≤ s ≤1) indicates a correlated barrier hopping conduction mechanism for charge carriers. Overall, these findings highlight CNCs as excellent candidate for antistatic applications, demonstrating conductivity ranging from 10−5 to10−9 Scm−1. The results also suggest CNCs can be used in energy storage applications due to their outstanding dielectric properties and low dielectric loss (<1).

On the instrument-dependent appearance of ion dissociation events in atom probe tomography mass spectra

The successful application of atom probe tomography (APT) relies on the accurate interpretation of the mass spectrum (i.e.m/z histogram) from a sample. Some materials yield mass spectra that are amenable to a straightforward peak assignment/ranging, however, there are many materials that produce mass spectra with features that defy simple interpretation. One such example is Ga2O3 which yields mass spectra containing several broad and difficult to interpret features. Herein, we study the GaO2+→ O1++ Ga1+ dissociation and we explain how this dissociation process gives rise to broad and previously unassigned features in the mass spectrum. Trajectory simulations are performed for the dissociation reaction utilizing realistic electrostatic models and compared to experiments using commercially available straight flight and reflectron based local electrode (LE) APT instruments. It is shown that the appearance of these features is strongly dependent on the specific design of the time-of-flight (ToF) mass analyzer. We explore how various experimental parameters can affect the appearance of the dissociation process in the one-dimensional (1D) mass spectrum and in the two-dimensional (2D) correlation histogram. While the focus of this work is on a particular dissociation process related to Ga2O3, the understanding gained in the course of these simulations and experiments should be applicable to the interpretation of dissociation processes in other materials.

Gravitational wave spectrum of chain inflation

Chain inflation is an alternative to slow-roll inflation in which the inflaton tunnels along a large number of consecutive minima in its potential. In this work we perform the first comprehensive calculation of the gravitational wave (GW) spectrum of chain inflation. In contrast to slow-roll inflation the latter does not stem from quantum fluctuations of the gravitational field during inflation, but rather from the bubble collisions during the first-order phase transitions associated with vacuum tunneling. Our calculation is performed within an effective theory of chain inflation which builds on an expansion of the tunneling rate capturing most of the available model space. The effective theory can be seen as chain inflation’s analog of the slow-roll expansion in rolling models of inflation. The near scale-invariance of the scalar power spectrum translates to a quasiperiodic shape of the inflaton potential in chain inflation, with the tunneling rate changing very slowly during the e-folds leading to cosmic microwave background observables. We show that chain inflation produces a very characteristic double-peak GW spectrum: a faint high-frequency peak associated with the gravitational radiation emitted during inflation, and a strong low-frequency peak associated with the graceful exit from chain inflation (marking the transition to the radiation-dominated epoch). There exist very exciting prospects to test the gravitational wave signal from chain inflation at the aLIGO-aVIRGO-KAGRA network, at LISA and /or at pulsar timing array experiments. A particularly intriguing possibility we point out is that chain inflation could be the source of the stochastic gravitational wave background recently detected by NANOGrav, PPTA, EPTA, and CPTA. We also show that the gravitational wave signal of chain inflation is often accompanied by running/ higher running of the scalar spectral index to be tested at future cosmic microwave background experiments. Published by the American Physical Society 2024

DFT and Model Hamiltonian Study of Optoelectronic Properties of Some Low-Symmetry Graphene Quantum Dots.

We have studied the electronic and optical properties of three low-symmetry graphene quantum dots (GQDs), with point-group symmetries C2v and C2h. For the calculations of linear optical absorption spectra, we employed both first-principles time-dependent density-functional theory (TDDFT) and the electron-correlated Pariser-Parr-Pople (PPP) model coupled with the configuration-interaction (CI) approach. In the PPP-CI approach, calculations were performed using both screened and standard parameters, along with efficiently incorporating electron correlation effects using multireference singles-doubles CI for both ground and excited states. We assume that the GQDs are saturated by hydrogen atoms at the edges, making them effectively polycyclic aromatic hydrocarbons (PAHs) dibenzo[bc,ef]coronene (also known as benzo(1,14)bisanthene, C30H14) and two isomeric compounds, dinaphtho[8,1,2abc;2',1',8'klm]coronene and dinaphtho[8,1,2abc;2',1',8'jkl]coronene with the chemical formula C36H16. The two isomers have different point group symmetries; therefore, this study will also help us understand the influence of symmetry on the optical properties. A common feature of the absorption spectra of the three GQDs is that the first peak representing the optical gap is of low to moderate intensity, while the intense peaks appear at higher energies. For each GQD, PPP model calculations performed with the screened parameters agree well with the experimental results of the corresponding PAH and also with the TDDFT calculations. To further quantify the influence of electron-correlation effects, we also computed the singlet-triplet gap (spin gap) of the three GQDs, and we found them to be significant.

Impact of minimal self disorders on naturalistic episodic memory in first-episode psychosis and parallels in healthy individuals with schizotypal traits

BackgroundSelf-disorders constitute a core feature of the schizophrenia spectrum, including early stages such as first-episode psychosis (FEP). These disorders impact the minimal Self, or bodily self-consciousness, which refers to the basic, pre-reflective sense of embodied experience. The minimal Self is intrinsically linked to episodic memory, which captures specific past experiences of the Self. However, research on this relationship in the schizophrenia spectrum remains scarce. This pilot study aimed to investigate how the minimal Self modulated episodic memory of naturalistic events in FEP, using immersive virtual reality. A secondary objective was to examine the relationships between sense of Self, embodiment, episodic memory, schizotypal personality traits in healthy participants (CTL), and psychopathology in FEP.MethodsA full-body illusion was induced in 10 FEP and 35 matched CTL, using a first-person avatar, with synchronous or asynchronous visuomotor stimulation (strong or weak embodiment conditions, respectively). Following embodiment induction, participants navigated a virtual city and encountered naturalistic daily life events, which were incidentally encoded. Episodic memory of these events was assessed through a comprehensive recognition task (factual and contextual information, retrieval phenomenology). Sense of Self, schizotypal personality traits, and psychopathology were assessed via self-reported questionnaires or clinical assessments.ResultsSynchronous visuomotor stimulation successfully induced a stronger sense of embodiment in both FEP and CTL. The strong embodiment condition was associated with reduced perceived virtual space occupation by the body in FEP. Under strong embodiment, FEP performed significantly worse than CTL in contextual information recognition, but their ratings for retrieval phenomenology were comparable to CTL. Conversely, under weak embodiment, FEP performed similarly to CTL in contextual information recognition, but they rated retrieval phenomenology significantly lower. For CTL, we observed a slight, though non-significant, enhancement in recognition memory under strong compared to weak embodiment. Additionally, higher schizotypy in CTL correlated with a diminished sense of Self and poorer episodic memory.ConclusionsDisturbances in the minimal Self in FEP are associated with episodic memory impairments. These findings emphasise the importance of targeting minimal Self-disorders in schizophrenia spectrum disorders, since episodic memory impairments may negatively affect patients’ quality of life and psychosocial outcomes. Additionally, they support a fully dimensional model of schizotypy.

Rapid Untargeted Puff‐by‐Puff Analysis of (Electronic) Cigarette Emissions by Concentric Dielectric Barrier Discharge Ionisation Mass Spectrometry

AbstractDespite the soaring popularity of e‐cigarettes among teenagers and young adults, our understanding of the full extent of their health hazards have remained limited. This is due to the vast complexities of e‐cigarette aerosols and the difficulty in their full characterisation. Conventional mass spectrometry methods of e‐cigarette analysis, though pioneering in driving political and medical discourse, have been limited in their capabilities to uncover all compounds in its emissions due to prominent limitations in experimental setup. To overcome this major hurdle, we have developed a setup for puff‐by‐puff analysis of electronic and conventional cigarette emissions by concentric dielectric barrier discharge ionisation mass spectrometry. In this pilot study, the simple setup of in‐line dilution and high‐resolution mass spectrometry analysis allowed us to directly uncover 225 compounds in e‐cigarette puffs across a wide spectrum of chemical classes in two sequential 5‐minute runs. These include acids, carbonyls, aromatic cyclics, heterocyclics, unsaturated and saturated hydrocarbons, alcohols, esters, alkaloids, sulfur‐containing compounds, oxides, and nitriles. As a result, our setup provided a significant improvement in rapid compound identification, and demonstrated a much broader chemical landscape in e‐cigarette emissions than previously reported.

Deep learning-based spectrum sensing and modulation categorization for efficient data transmission in cognitive radio

Abstract One prominent feature of cognitive radio (CR) involves spectrum sensing (SS), which allows licensed primary users to remain unaffected by secondary users’ ability to discover and exploit unoccupied frequency bands. Spectrum sensing enhances the use of spectrum in CR devices, increasing their adaptability and efficiency in wireless communication systems. The rise of wireless equipment and the advent of IoT technologies compound this need for flexibility. Over time, the fixed allocation of frequencies has led to inefficiencies and underutilization as bandwidth needs increase. Deep learning and artificial intelligence have improved spectrum sensing by increasing detection probability of primary users’ presence under noisy environments, enabling cognitive radio systems to respond intelligently to fluctuations in RF environments. This article is concerned with deep learning techniques for spectrum sensing and modulation categorization with CBRT structure, which combines convolutional neural networks (CNNs), bidirectional recurrent neural networks (BRNNs), and transformer networks (TNs) to improve spectrum sensing. CNNs are responsible for performing spectrum feature extraction; BRNNs are used to capture temporal dependencies; and TNs are good at long range dependencies. Better performance for this model is aimed by integrating the three architectures described. In the proposed work, six digital modulation schemes were considered, for spectrum sensing. The sensing of spectrum in this model is performed using the RadioML2016.10B open-source dataset and performance metrics like the Jaccard Index (JI), Fowlkes’s Mallows Index, and F1 Score. Modulation classification has been performed using MIGOU-MOD open-source dataset. The proposed model exhibits good detection probability and low sensing error, unlike other methods at lower SNR.

Spectral Signatures of Nontrivial Topology in a Superconducting Circuit

Topology, like symmetry, is a fundamental concept in understanding general properties of physical systems. In condensed matter, nontrivial topology may manifest itself as singular features in the energy spectrum or the quantization of electrical properties such as conductance and magnetic flux. Using microwave spectroscopy, we determine that a superconducting circuit with three Josephson tunnel junctions in parallel can possess degeneracies indicative of nontrivial topology. We identify three topological invariants, one of which is related to a hidden quantum mechanical supersymmetry. Measurements show that devices fabricated in different topological regimes fall on a simple phase diagram, which should be robust to junction imperfections and geometric inductance. Josephson tunnel junction circuits, which are readily fabricated with conventional microlithography techniques, allow access to a wide range of topological systems that may have no condensed matter analog. Notable spectral features of these circuits, such as degeneracies and flat bands, may find use in quantum information, sensing, and metrology. Published by the American Physical Society 2024

Overview of hepatocarcinogenesis focusing on cellular origins of liver cancer stem cells: a narrative review.

Hepatocellular carcinoma (HCC) accounts for 85% to 90% of primary liver cancers and generally has a poor prognosis. The hierarchical model, which posits that HCC originates from liver cancer stem cells (CSCs), is now widely accepted, as it is for other cancer types. As CSCs typically reside in the G0 phase of the cell cycle, they are resistant to conventional chemotherapy. Therefore, to effectively treat HCC, developing therapeutic strategies that target liver CSCs is essential. Clinically, HCCs exhibit a broad spectrum of pathological and clinical characteristics, ranging from well-differentiated to poorly differentiated forms, and from slow-growing tumors to aggressive ones with significant metastatic potential. Some patients with HCC also show features of cholangiocarcinoma. This HCC heterogeneity may arise from the diverse cellular origins of liver CSCs. This review explores the normal physiology of liver regeneration and provides a comprehensive overview of hepatocarcinogenesis, including cancer initiation, isolation of liver CSCs, molecular signaling pathways, and microRNAs. Additionally, the cellular origins of liver CSCs are reviewed, emphasizing hematopoietic and mesenchymal stem cells, along with the well-known hepatocytes and hepatic progenitor cells.

Large depth-of-field ultraviolet-visible photoacoustic histologic and microvascular imaging in vivo

Photoacoustic microscopy (PAM) can image diverse biological microstructures by harnessing characteristic optical absorption spectra of intrinsic nonfluorescent biomolecules. We incorporate ultraviolet (UV) and visible pulsed lasers into a PAM to develop an UV-visible PAM for label-free histologic and microvascular imaging, where the cell nuclei and blood vessels are specifically captured at high contrast relying on strong optical absorption of DNA/RNA and hemoglobin at 266 and 532 nm wavelengths, respectively. Moreover, two diffractive optical elements operating at UV and visible spectra are designed for engineering the excitation beams, significantly enlarging depths of field (DOFs &amp;gt; 200 μm). The UV-visible PAM demonstrates combined capabilities of dual imaging contrast, elongated DOFs, and micrometer-scale lateral resolution for delineating the spatial microarchitectures of both cell nuclei and blood vessels that are at different depth locations in the biological specimens with uneven surfaces. Longitudinal monitoring of the trauma is performed in mouse ear in vivo. Potentially, our UV-visible PAM could offer comprehensive histologic and microvascular information in a broad range of biomedical studies.

Cholate Shunt, Oral Cholate Challenge and Endoscopic Lesions of Portal Hypertension: The SHUNT-V Study.

The accuracy of current criteria for ruling out large oesophageal varices (LEV) and other endoscopic lesions of portal hypertension (PH) may be compromised by obesity and MASLD/MASH. In the US multicentre SHUNT-V study, we evaluated the disease severity index (DSI) for detecting LEV and other lesions of PH at endoscopy. Subjects were adults with compensated cirrhosis scheduled for endoscopy to screen for varices. DSI was calculated from clearances of labelled cholates after oral and intravenous administration. DSI ≤ 18.3 was evaluated as a cut-off for ruling out LEV with acceptance criteria of negative likelihood ratio < 0.52 and sensitivity > 85%. SHUNT-V enrolled 306 subjects; 275 had both DSI and endoscopy, and 238 had Child-Pugh A cirrhosis (52.1% MASLD/MASH, 25.2% chronic hepatitis C and 15.6% alcoholic liver disease; 87% were overweight, 64% were obese and 54% had diabetes). AUROCs for DSI ranged from 0.81 to 0.82 for LEV and 0.79 to 0.80 for all significant PH lesions. DSI 18.3 had sensitivity 96.3%-100% for LEV and 97.3%-100% for all significant PH lesions. If DSI ≤ 18.3 were used as the sole determinant to defer EGD, 27%-35% of EGDs could have been avoided with 0%-3.7% of LEV and 0%-2.7% of all significant PH lesions missed. HepQuant DSI predicts the likelihood of LEV and significant PH lesions across a spectrum of patient characteristics and disease aetiologies. DSI, based on liver function and portal-systemic shunting, can aid in the decision to defer endoscopy for varices in patients with Child-Pugh A cirrhosis. The SHUNT-V study was registered at ClinicalTrials.gov (NCT03583996).

Portable near-infrared detection to replace color tests in an analytical scheme for forensic drug identification

In the ever-changing drug market the popularity and availability of substances is dynamic. In the Netherlands, ketamine and various cathinones have recently seen increased prevalence. It is crucial for law enforcement to quickly obtain an initial indication of the identity of a substance. This first test also serves as quality control for subsequent confirmation with GC–MS analysis. Traditionally, color tests have been used for these purposes. While these tests are quick and inexpensive, they have the disadvantage of reacting only to a few traditional drugs. Suitable color tests are not available for many new psychoactive substances (NPS). Near-infrared (NIR) spectroscopy is a rapid technique that provides a characteristic spectrum for organic compounds. This technique is more versatile than color tests and can adapt more quickly to market changes by incorporating reference spectra into the library. This study demonstrates the feasibility of obtaining a good quality NIR spectrum from a 20 mg sub-sample in a test tube. This was achieved by scanning the test tube through the glass bottom. In the routine analytical scheme, these test tubes were subsequently batchwise analyzed by GC–MS. From the NIR spectra, 84 % true positive and 100 % true negative results were achieved on 516 casework samples, including identification of substances without available color tests like ketamine. Missed false negatives primarily involved new substances absent from the library, emphasizing the need for continuous library updates. NIR’s adaptability to market changes is crucial, allowing the inclusion of new substances as they emerge. This method enhances law enforcement’s ability to make informed decisions, aiding in the indictment process.

High-sensitivity and high-resolution triboelectric acoustic sensor for mechanical equipment monitoring

Traditional sensors for mechanical equipment monitoring often face challenges such as high cost, difficult installation and removal, and reliance on power sources. Herein, we introduced a polyvinylidene fluoride (PVDF) nanofiber membrane doped with BaTiO3 for constructing a triboelectric acoustic sensor (PB-TAS), innovatively designed for monitoring and recognizing the working conditions of mechanical equipment. This represents the first application of a triboelectric acoustic sensor for mechanical equipment monitoring. The PB-TAS demonstrates an ultra-broad frequency response range of 20–20,000 Hz, effectively covering the characteristic frequency spectrum (within 2000 Hz) during piston pump operation. It also exhibits extraordinarily sensitivity of 4.40 V Pa−1 at 85 dB, and ultra-high frequency resolution down to 0.1 Hz, enabling precise matching of the piston pump's acoustic characteristic frequencies. Integrated with deep learning techniques, the PB-TAS achieves real-time recognition of 15 distinct working conditions of a piston pump, with an impressive accuracy of 95.2 %. The PB-TAS, boasting exceptional sensitivity and frequency resolution, offers a cost-effective, miniaturized, self-powered, non-contact, and highly accurate solution for intelligent monitoring of hydraulic piston pumps.

Asteroid material classification based on multi-parameter constraints using artificial intelligence

Material types of asteroids provide key clues to their evolutionary history and contained resources. The Gaia mission has released extensive low-resolution spectral observation data of small Solar System bodies. However, methods for classifying asteroids based on low-resolution space-based spectra are still inadequate, and do not fully leverage the complementary features of spectra and multiple intrinsic attributes of asteroids to achieve precise material classification. Our goal is to propose a method with a higher generalization accuracy for asteroid material classification by integrating multi-source information, identifying optimal feature combinations for model inputs, and deepening the understanding of relationships among asteroid parameters. The effective asteroid photometric, physical, and orbital parameters were screened using the information gain ratio and Spearman's rank correlation coefficient. Then, artificial intelligence techniques were employed to combine asteroid spectra with the selected various parameters for six-class material classification. By comparing five machine learning models, we identified network structures with higher validation accuracy and stable generalization performance. Meanwhile, feature ablation experiments were conducted to determine the input parameter combinations suitable for different scenarios. Finally, based on the statistical results and model outputs, the constraint relationships among asteroid parameters were visualized and analyzed. The proposed AsterRF model achieved a validation accuracy of 92.2&lt;!PCT!&gt;, an improvement of approximately 7.8 percentage points compared to existing methods that use only spectra. V-type asteroids exhibited the highest classification accuracy, followed by A-type and D-type. X-type asteroids had the lowest precision and recall, and were easily confused with C-type. The model generally showed higher classification confidence for S-type asteroids. The top five attributes that the model focused on are the phase slope parameter (G), orbital type, albedo, H magnitude, and effective diameter. Additionally, the correlations between asteroid materials and other parameters were generally below 0.4. Incorporating optimal asteroid parameter combinations can significantly enhance classification accuracy based on spectra. A dual-channel network that processes spectra and parameter inputs separately, and employs a self-attention mechanism for feature fusion is effective in combining multi-source asteroid information. Both the statistical correlations and model performance-based importance rankings of parameters contribute to understanding the constraint relationships among asteroid attributes.

Exploring nonlinear correlations among transition metal nanocluster properties using deep learning: a comparative analysis with LOO-CV method and cosine similarity

A novel approach is introduced for the rapid and accurate correlation analysis of nonlinear properties in Transition Metal (TM) clusters utilizing the Deep Leave-One-Out Cross-Validation technique. This investigation demonstrates that the Deep Neural Network (DNN)-based approach offers a more efficient predictive method for various properties of fourth-row TM nanoclusters compared to conventional Density Functional Theory methods, which are computationally intensive and time-consuming. The feature space, also known as descriptors, is established based on a broad spectrum of electronic and physical characteristics. Leveraging the similarities among these clusters, the DNN-based model is employed to explore the correlations among TM cluster properties. The proposed method, in conjunction with cosine similarity, achieves remarkable accuracy up to 10-9 for predicting total energy, lowest vibrational mode, binding energy, and HOMO-LUMO energy gap of TM2, TM3, and TM4nanoclusters. By analyzing correlation errors, the most closely coupled TM clusters are identified. Notably, Mn and Ni clusters exhibit the highest and lowest levels of energy coupling with other TMs, respectively. Generally, energy prediction for TM2, TM3, and TM4clusters exhibit similar trends, while an alternating behavior is observed for vibrational modes and binding energies. Furthermore, Ti, V, and Co demonstrate the highest binding energy correlations with TM2, TM3, and TM4sets, respectively. Regarding energy gap predictions, Ni exhibits the strongest correlation in the smallest TM2clusters, while Cr shows the highest dependence in TM3and TM4sets. Lastly, Zn displays the largest error in HOMO-LUMO energy gap across all sets, indicating distinctive independent energy gap characteristics.

Optimization of curdlan production and ultrasound assisted extraction processes from Priestia megaterium

In this study, the upstream and downstream production processes of curdlan from Priestia megaterium were optimized to enhance its yield. Additionally, a novel extraction method was developed for curdlan recovery. Optimization studies were conducted using Central composite design (CCD). Curdlan yield improved from 0.15 g/L (unoptimized) to 0.46 g/L (3-fold increase) when fermentation was carried out in CCD-optimized media of (w/v) sucrose 20%, urea 0.1%, KH2PO4 0.02%, agitation speed 250 rpm. To further enhance curdlan yield during extraction, ultrasonication was incorporated as a novel step into the conventional method of acid/alkali-assisted curdlan recovery. A two-step optimization was chosen for extraction, namely, one-factor-at-a-time (OFAT) and CCD, wherein the optimized extraction parameters were determined to be 25 s sonication, 1 N NaOH, and 2 h solubilization time. The curdlan yield improved by 1.5-fold (0.70 g/L) post optimization, in comparison with unoptimized conventional extraction step. Finally, the biopolymer was validated through characterization by nuclear magnetic resonance (NMR) which showed characteristic curdlan spectra in the13C and1H NMR studies. To the best of our knowledge, this study represents the first documented report on curdlan extraction using this novel method of ultrasonication.

Anti-microbial and anti-hepatitis-A activities of Asphodelus refractus boiss. extract and pure isolated metabolites

Four compounds, including N-trans-feruloyltyramine, were isolated from Asphodelus refractus extract and elucidated using UV, IR, MS, and NMR techniques. The trans-feruloyltyramine displayed advantageous structural characteristics, including a Log P of 2.58 and compliance with Lipinski's rules. With 7 rotatable bonds, 23 heavy atoms, 12 aromatic atoms, and a surface area of 78.79 Ų, it effectively binds to the target 3C-proteinase receptor. Trans-feruloyltyramine exhibited characteristic IR and 1H-NMR spectra, confirming the presence of a conjugated double bond, feruloyl and tyramine moieties, and an amide structure. The antimicrobial assay revealed mild antifungal effects for the plant extract and isolated compounds against Candida tropicalis with inhibition zone diameters (IZD) of 12–15 mm, compared to ketoconazole (IZD, 21 mm). The extract exhibited better inhibition for Gram-ve compared to the Gram+ve bacteria. The findings indicated that chloroform extract and trans-feruloyltyramine have marked anti-hepatitis-A activity, with inhibition percentages at 69.34 ± 3.92 and 56.91 ± 1.75, respectively, at their maximum non-cytotoxic concentrations (MNCC), compared to amantadine (83.24 %). They have also exhibited non-selective anti-HAV activity with EC50 values of 308.69 ± 8.43 and 317.94 ± 6.88 μg/mL, respectively. In comparison to other isolated compounds, docking predicted a higher binding affinity for trans-feruloyltyramine towards hepatitis A virus (HAV) 3C-proteinase, with binding energy at DG -8.3 Kcal/mol.

A 2DEG‐Based GaN‐on‐Si Terahertz Modulator with Multi‐Mode Switchable Control

AbstractAs terahertz (THz) technology has been widely considered as a key candidate for future sixth‐generation wireless communication networks, THz modulators show profound significance in wireless communication, data storage, and imaging. In special, dynamic tuning of THz waves through 2D electron gas (2DEG) incorporated with a hybrid design of metasurface has attracted keen interest due to the combined merits of deliberate structural design, rapid switching speed and the process compatibility. Current meta‐modulator enables very high modulation depth but encounter limited bandwidth. In this paper, by taking into account the co‐functional effects of temperature and voltage‐dependent dynamic control on transmission amplitude, a 2DEG‐based GaN‐on‐Si modulator with two switchable operational states (or four modes) of active wave control is proposed. Under cryogenic temperature conditions, the proposed device exhibits prominent 2D plasmons characteristics with switchable transitions between the gated mode and ungated mode for active control. Under room temperature conditions, the proposed device exhibits non‐resonance broadband spectra characteristics with tunable transitions between the linearity mode and depletion mode for transmission control. The scheme provides an option for the development of the actively tunable THz meta‐modulator and paves a way for the robust multifunctionality of electrically controllable THz switching, and biosensors.

Luminescent rare earth complexes of Ce3+, Eu2+and Yb2+with spiro-bis(pyrazolyl)borate ligands containing C–H···M interactions

Luminescent rare earth complexes based on interconfigurational 5d-4f transition have characteristics of adjustable emission spectra and short excited state lifetimes, showing potential applications in display, lighting and other fields. In this work, nine Ce3+, Eu2+ and Yb2+ complexes with spiro-bis(pyrazolyl)borate ligands, where pyrazolyl stands for pyrazolyl, 3-methylpyrazolyl, or 3,5-dimethylpyrazolyl, were designed and synthesized. Ce3+ complexes show emission colors of blue, with photoluminescence quantum yields (PLQYs) of 94%–100%; Eu2+ complexes show emission colors of yellow-green, with PLQYs of 60%–78%; Yb2+ complexes show emission colors of orange or red, with PLQYs of 3%–4%. In addition, the crystal structures and theoretical calculation results show C–H···M interactions between bridge-head H atoms of the spirane and central metal ions in these complexes. It is found that the introduction of spirane with large steric hindrance and the existence of C–H···M interactions can improve the stability of the complexes in air atmosphere.