Spectroscopy Research Articles

Optical spectroscopy of Nd3+-doped cadmium-rich borate glasses for near-infrared laser applications

AbstractThe structural and spectroscopic properties of Nd3+-activated cadmium-rich borate (inverted) glasses are analyzed for near-infrared laser applications. The evaluation of the optimal glass-emitting sample by the Judd–Ofelt (JO) theory revealed JO parameter values of 4.56 × 10–20 cm2 (Ω2), 2.56 × 10–20 cm2 (Ω4), and 3.84 × 10–20 cm2 (Ω6). The Ω2 value, along with the experimental oscillator strength, suggested that the cadmium-rich borate glass could provide a more asymmetrical Nd3+ environment than other borate glasses like lithium-strontium-zinc, sodium-calcium, and lithium-lead-aluminum. In addition, the quality spectroscopy factor (χ = Ω4/Ω6) of 0.67 suggested that the 4F3/2 → 4I11/2 emission could be more suitable for laser applications. The stimulated emission cross-section (σp), theoretical quantum yield (ηQ), gain bandwidth (σp × Δλem), and optical gain (σp × τrad) laser parameters were close to those reported in sodium-calcium-borate, zinc-aluminum-barium-borate, and bismuth-borate glasses, while the non-radiative rate (WNR) and emission intensity saturation (IS) resulted to be lower. The emission spectra, under 808 nm laser excitation, displayed the featured neodymium 4F3/2 → 4I9/2,11/2,13/2 transitions, being the 4F3/2 → 4I11/2 (1058 nm) transition the more dominant one, in agreement with the χ parameter value. Nd3+ contents higher than 1.4 mol% led to emission quenching due to the increment of the cross-relaxation and/or energy migration rate. Such processes, according to the Inokuti–Hirayama model, were mainly mediated by electric dipole–dipole interactions within Nd–Nd clusters.

Single gold nanowire-nanoparticles conjugated system: Fabrication and sensing application

Single-entity-based sensing platform has many advantages for real-time assays, such as single-molecule analysis, or targets detection in confined environment. In this contribution, a new single Au nanowire (NW) – Au@Pt/Au nanoparticles (Au@Pt/Au NPs) conjugated system was established and used for the detection of thrombin by using surface-enhanced Raman spectroscopy (SERS) technique. This method was mainly based on electrostatic attraction between the capture (thrombin aptamer) and probe molecules (crystal violet, CV) on the surface of Au NW - Au@Pt/Au NPs conjugation, reducing the adsorption of CV molecules on conjugation surface, and resulting the decrease of SERS signals. The addition of thrombin could bind with thrombin aptamer due to specific interaction, leading to the decrease of electrostatic effect from thrombin aptamer for CV diffusion to conjugation surface, and the SERS signals could be recovered. This thrombin sensing method showed high sensitivity and selectivity, and the linear range for thrombin assay was 0.01 nM–100 nM with a detection limit of 1.35 pM. This single Au NW - Au@Pt/Au NPs conjugation has more SERS hotspots and small overall size, which offers unparalleled advantages over other sensing system and can be applicable for real time analysis, especially at single molecule/cell level.

Defect-Engineered Coordination Compound Nanoparticles Based on Prussian Blue Analogues for Surface-Enhanced Raman Spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for label-free chemical analysis. The emergence of nonmetallic materials as SERS substrates, offering chemical signal enhancements, presents an exciting direction for achieving reproducible and biocompatible SERS, a challenge with traditional metallic substrates. Despite the potential, the realm of nonmetallic SERS substrates, particularly nanoparticles, remains largely untapped. Here, we present defect-engineered coordination compounds (DECCs) based on Prussian blue analogues (PBAs) as a class of nonmetallic nanoparticle-based SERS substrates. We demonstrate the utility and flexibility of the DECC template by incorporating various metal (M) elements into PBAs to synthesize nanoparticles that deliver substantial chemical mechanism (CM)-based enhancements to the Raman signal with a ∼ 108-fold increase. The introduction of the M-PBA-based DECC nanoparticles as a class of SERS substrates represents a pioneering stride, enabling the straightforward and systematic exploration of a library of compounds for SERS-based analysis of a wide range of target molecules, especially biomolecules.

An X-ray flaring event and a variable soft X-ray excess in the Seyfert LCRS B040659.9–385922 as detected with eROSITA

Context. Extreme continuum variability in extragalactic nuclear sources can indicate extreme changes in accretion flows onto supermassive black holes. Aims. We explore the multiwavelength nature of a continuum flare in the Seyfert LCRS B040659.9−385922. The all-sky X-ray surveys conducted by the Spectrum Roentgen Gamma (SRG)/extended ROentgen Survey with an Imaging Telescope Array (eROSITA) showed that its X-ray flux increased by a factor of roughly five over six months, and concurrent optical photometric monitoring with the Asteroid Terrestrial-impact Last Alert System (ATLAS) showed a simultaneous increase. Methods. We complemented the eROSITA and ATLAS data by triggering a multiwavelength follow-up monitoring program (X-ray Multi-Mirror Mission: XMM-Newton, Neutron star Interior Composition Explorer: NICER; optical spectroscopy) to study the evolution of the accretion disk, broad-line region, and X-ray corona. During the campaign, X-ray and optical continuum flux subsided over roughly six months. Our campaign includes two XMM-Newton observations, one taken near the peak of this flare and the other taken when the flare had subsided. Results. The soft X-ray excess in both XMM-Newton observations was power law-like (distinctly nonthermal). Using a simple power law, we observed that the photon index of the soft excess varies from a steep value of Γ ∼ 2.7 at the flare peak to a relatively flatter value of Γ ∼ 2.2 as the flare subsided. We successfully modeled the broadband optical/UV/X-ray spectral energy distribution at both the flare peak and post-flare times with the AGNSED model, incorporating thermal disk emission into the optical/UV and warm thermal Comptonization in the soft X-rays. The accretion rate falls by roughly 2.5, and the radius of the hot Comptonizing region increases from the flaring state to the post-flare state. Additionally, from the optical spectral observations, we find that the broad He IIλ4686 emission line fades significantly as the optical/UV/X-ray continuum fades, which could indicate a substantial flare of disk emission above 54 eV. We also observed a redshifted broad component in the Hβ emission line that is present during the high flux state of the source and disappears in subsequent observations. Conclusions. A sudden strong increase in the local accretion rate in this source manifested itself via an increase in accretion disk emission and in thermal Comptonized emission in the soft X-rays, which subsequently faded. The redshifted broad Balmer component could be associated with a transient kinematic component distinct from that comprising the rest of the broad-line region.

SERS calibration substrate with a silent region internal standard for reliable simultaneous detection of multiple antibiotics in water

As the extensive use of antibiotics has led to the rapid spread of antibiotic resistance, there is an urgent need for quantitative assessment of antibiotic residues in the environment. Surface-enhanced Raman spectroscopy (SERS) has emerged as a rapid and cost-effective detection method, but it suffers from the high variability in signal intensities, its quantitative detection remains challenging. Herein, we have developed a SERS calibration substrate with a silent region internal standard, enabling simultaneous and reliable quantitative detection of three commonly antibiotics of penicillin potassium (PP), tetracycline hydrochloride (TCH) and levofloxacin (LEV). The calibration substrate is made by assembling Au @ 4-mercaptobenzonitrile (4-MBN) @ SiO2 on silicon wafer. The chemically-inert silica shell allows the substrate to remain SERS active for more than 24 weeks in the air. The vC ≡ N of 4-MBN in the silent region of 1800–2800 cm−1 provides an effective reference for correcting Raman signal fluctuations. The relative SERS intensity by normalizing to the internal standard (IS) of vC ≡ N shows a linear response against to the logarithmic concentration with a correlation coefficient of 0.997, 0.976, 0.998 and a limit of detection (LOD) of 26.9, 28.2, 2.4 nM for PP, TCH and LEV, respectively. Furthermore, simultaneous detection and principal component analysis (PCA) of these antibiotics in lake water with a concentration range of 1–100 mg/L can achieve a sensitivity and specificity of 100 %. This novel quantitative technique of using this SERS calibration substrate shows promises as a high-throughput platform for multiple trace antibiotics analysis.

Real-time measurement of metals in submicron aerosols with particle-into-liquid sampler combined with micro-discharge optical emission spectroscopy.

The paper presents a novel technique for quantifying trace metals in aerosol samples in real time. Airborne metals were continuously collected for one week near the Baltic Sea in Finland using a particle-into-liquid sampler (PILS). The collected liquid samples were analyzed for metals using micro-discharge optical emission spectroscopy (µDOES). The micro-discharge analyzer is designed to perform real-time, on-site measurements of metal concentrations in aqueous solutions. Currently, µDOES can provide online measurements of 30 metals, with typical detection limits from 0.01µg/m3 to 0.06µg/m3 with a long-term repeatability less than 5%. The novelty of this analyzer lies in its compact design, rapid detection capabilities, and ease of operation and maintenance. Several metals, including potassium (K), calcium (Ca), sodium (Na), aluminum (Al), magnesium (Mg), and copper (Cu), were measured in the aerosol samples collected using PILS. The results indicate that this approach has significant potential for future automated online monitoring of airborne metal concentrations, facilitating investigations into their sources and daily variations. The development of real-time technologies for rapid, online, and accurate atmospheric aerosol measurements is essential for advancing climate change research. Such advancements allowing for continuous real-time data enhance our understanding of aerosol dynamics, improve source identification, and inform public health and environmental policies, ultimately contributing to more effective climate change monitoring and mitigation.

Measurements of Heavy Elements and Radioactivity Concentration Levels in Types of Fertilizers and Pesticides Commonly Used in Sana’a Yemen

This study aims to measure heavy metals (HMs) concentration and radioactivity concentration levels in fertilizer and pesticide samples collected from local markets in Sana’a, Yemen. Gamma spectroscopy employing a Survey Sodium Iodide NaI (TI) RIIDEYM-G detector was used for radiation and dose assessments. As for heavy metal analyses, this study used Inductively Coupled Optical Emission Spectroscopy (ICP-OES). Thirty samples were collected from local markets in Sana’a, Yemen. Radioactivity measurements show low levels of radioactivity concentration. The average dose rates due to fertilizer and pesticide samples were 1.14 and 1.07 mSv/y, respectively. These values are nearly equal to the recommended annual radiation dose rate limit (1mSv/y) recommended by the International Commission on Radiological Protection (ICRP). For heavy metal analysis, it was found that the average concentrations of heavy metals (HMs) (cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), nickel (Ni), lead (Pb), and zinc (Zn)) in the fertilizer samples were 1.08 mg/kg, 4.944 mg/kg, 13.8495 mg/kg, 274.371 mg/kg, 3.30446 mg/kg, 15.35686 mg/kg, and 24.4547 mg/kg, respectively. Heavy metal concentrations in fertilizer samples were mainly less than European (EU) guideline limits, with the exception of a few samples. The average concentrations of heavy metals (Cd, Cr, Cu, Fe, Ni, Pb, and Zn) in the pesticide samples were 0.8 mg/kg, 0.5784 mg/kg, 52.280 mg/kg, 73.4651 mg/kg, 2.4482 mg/kg, 3.199 mg/kg, and 1.1309 mg/kg, respectively. The heavy metal concentrations in pesticide samples were less than the corresponding values in fertilizer samples.

Validation of GEMS tropospheric NO2 columns and their diurnal variation with ground-based DOAS measurements

Abstract. Instruments for air quality observations on geostationary satellites provide multiple observations per day and allow for the analysis of the diurnal variation in important air pollutants such as nitrogen dioxide (NO2). The South Korean instrument GEMS (Geostationary Environmental Monitoring Spectrometer), launched in February 2020, is the first geostationary instrument that is able to observe the diurnal variation in NO2. The measurements have a spatial resolution of 3.5 km × 8 km and cover a large part of Asia. This study compares 1 year of tropospheric NO2 vertical column density (VCD) observations from the operational GEMS L2 product, the scientific GEMS IUP-UB (Institute of Environmental Physics at the University of Bremen) product, the operational TROPOspheric Monitoring Instrument (TROPOMI) product, and ground-based differential optical absorption spectroscopy (DOAS) measurements in South Korea. The GEMS L2 tropospheric NO2 VCDs overestimate the ground-based tropospheric NO2 VCDs with a median relative difference of +61 % and a correlation coefficient of 0.76. The median relative difference is −2 % for the GEMS IUP-UB product and −16 % for the TROPOMI product, with correlation coefficients of 0.83 and 0.89, respectively. The scatter in the GEMS products can be reduced when observations are limited to the TROPOMI overpass time. Diurnal variations in tropospheric NO2 VCDs differ by the pollution level of the analyzed site but with good agreement between the GEMS IUP-UB and ground-based observations. Low-pollution sites show weak or almost no diurnal variation. In summer, the polluted sites show a minimum around noon, indicating the large influence of photochemical loss. Most variation is seen in spring and autumn, with increasing NO2 in the morning, a maximum close to noon, and a decrease towards the afternoon. Winter observations show rather flat or slightly decreasing NO2 throughout the day. Winter observations under low-wind-speed conditions at high-pollution sites show enhancements of NO2 throughout the day. This indicates that under calm conditions, dilution and the less effective chemical loss in winter do not balance the accumulating emissions. Diurnal variation observed at a low-pollution site follows seasonal wind patterns. A weekday–weekend effect analysis shows good agreement between the different products. However, the GEMS L2 product, while agreeing with the other data sets on weekdays, shows significantly less reduction on weekends. The influence of the stratospheric contribution and the surface reflectivity product on the satellite tropospheric NO2 VCD products is investigated. While the TM5 model's stratospheric VCDs, used in the TROPOMI product, are too high, resulting in tropospheric NO2 VCDs that are too low and even negative, when used in the GEMS IUP-UB retrieval, the GEMS L2 stratospheric VCD is too low. Surface reflectivity comparisons indicate that the GEMS L2 reflectivity makes a large contribution to the observed overestimation and scatter.

Rapid detection and spectroscopic feature analysis of mineral content in camel milk using fourier-transform mid-infrared spectroscopy and traditional machine learning algorithms

Camel milk is rich in nutrients and bioactive factors, with mineral content generally higher than that of cow milk, but there is currently no internationally established, rapid, batch-testing method for the mineral content. This study collected samples of camel milk from 113 locations in Xinjiang, China. For the first time internationally, based on the true mineral values determined by ICP-OES (Inductively Coupled Plasma Optical Emission Spectroscopy) and the extracted mid-infrared spectra data, a quantitative prediction model for 10 key minerals (Ca, Fe, K, Mg, Mn, Na, P, Sr, Zn, and Se) was established using Fourier-Transform Mid-Infrared Spectroscopy (FT-MIRS) and the traditional machine learning algorithm Partial Least Squares Regression. The Rt2 of the test set ranged from 0.61 to 0.91, RMSEt ranged from 2.21ug/kg(Se) to 197.08 mg/kg(K) and the RPDt from 1.59 to 3.28. In addition, the distribution, patterns, and correlations of mineral-related characteristic wavenumbers in camel milk were summarized. This study opens a new avenue for the rapid detection of minerals in camel milk and fills the research gap in in using FT-MIRS to detect mineral content in camel milk.

Experimental and Statistical Analysis of Iron Powder for Green Heat Production

In the current investigation, a novel methodology was employed to assess iron powder as a recyclable and sustainable energy carrier. Concurrently, an examination of the modeling of iron powder ignition and the ensuing heat output from the burner was undertaken. The flame temperature was determined by examining the light intensity emitted by the particles as they melted, which is directly related to the particle’s cross-sectional area. An account of the characterization of the experimental procedure, validation, and calibration is presented. Through measurements, distinct one-to-one correlations have been established between the scales of flame combustion and the temperatures of particles of varying sizes of iron. Additionally, a theoretical model for the combustion of expanding particles, particularly iron, within the diffusion-limited regime has been rigorously developed. This model delves into the spectra acquired from particle flames within the burner, utilizing Partial Least Squares Regression (PLSR) and Principal Component Analysis (PCA). This study investigates the use of optical fiber spectroscopy to predict flame temperature and assess iron powder size. The aim was to investigate how different sizes of iron powder affect flame temperature and to create calibration models for non-destructive prediction. The study shows that smaller particles had an average temperature of 1381 °C while larger particles reach up to 1842 °C, demonstrating the significant impact of particle size on combustion efficiency. The results were confirmed using advanced statistical methods, including PLSR and PCA, with PCA effectively differentiating between particle sizes and PLSR achieving an R2 value of 0.90 for the 30 µm particles.

Valorisation of acid mine drainage through effective recovery of selected rare earth elements using cationic resins

Acid mine drainage (AMD) presents a challenge to the environment if not well managed, but it also presents an opportunity for the recovery of economically valuable products, including rare earth elements (REEs), which are critical for the development of advanced, and green technologies. REE concentrations in AMD samples from coal and gold mines were determined using inductively coupled plasma optical emission spectroscopy (ICP-OES) and their sorption by different cationic resins (CHT4083, CHP4502 and CHP00712) was evaluated. Optimum conditions for the sorption of the REEs by these resins were determined through batch experiments and desorption of the REEs from the resins using different concentrations of sulphuric acid (H2SO4) solutions. Coal mine drainage (CMD), with a low pH of 2.37, had higher amounts of REEs (ΣREE 226.3732 mg/L) than AMD from the gold mines (ΣREE 4.9705 mg/L), with a pH of 3.21. A REE sorption efficiency of up to 98% was obtained with CHP4502 and CHP00712 resins and further optimisation of CHP00712 revealed that a resin volume of 250 mL and a contact time of 10 min were required to successfully remove REEs from 500 mL AMD. The sorption capacities of the resin for the selected REEs were 3.88 mg/g, 0.88 mg/g, 1.37 mg/g, 3.18 mg/g, 0.67 mg/g, 0.01 mg/g and 0.27mg/g for Pr, Gd, Nd, Ce, Sm, Eu and Y, respectively. Elution of the resin with a 0.5 N solution of sulphuric acid desorbed the REEs. AMD from coal mines could be an alternate source of REEs and cationic resins can be used to recover these REEs from the CMD. Further investigations, including impregnation of resin to improve its sorption capacity, and temperature effects on the sorption process, are recommended.

Three-Dimensional (3D) Surface-Enhanced Raman Spectroscopy (SERS) Substrates for Sensing Low-Concentration Molecules in Solution.

The use of surface-enhanced Raman spectroscopy (SERS) in liquid solutions has always been challenging due to signal fluctuations, inconsistent data, and difficulties in obtaining reliable results, especially at very low analyte concentrations. In our study, we introduce a new method using a three-dimensional (3D) SERS substrate made of silica microparticles (SMPs) with attached plasmonic nanoparticles (NPs). These SMPs were placed in low-concentration analyte solutions for SERS analysis. In the first approach to perform SERS in a 3D environment, glycerin was used to immobilize the particles, which enabled high-resolution SERS imaging. Additionally, we conducted time-dependent SERS measurements in an aqueous solution, where freely suspended SMPs passed through the laser focus. In both scenarios, EFs larger than 200 were achieved, which enabled the detection of low-abundance analytes. Our study demonstrates a reliable and reproducible method for performing SERS in liquid environments, offering significant advantages for the real-time analysis of dynamic processes, sensitive detection of low-concentration molecules, and potential applications in biomolecular interaction studies, environmental monitoring, and biomedical diagnostics.

Rapid and accurate bacteria identification through deep-learning-based two-dimensional Raman spectroscopy

Surface-enhanced Raman spectroscopy (SERS) offers a distinctive vibrational fingerprint of the molecules and has led to widespread applications in medical diagnosis, biochemistry, and virology. With the rapid development of artificial intelligence (AI) technology, AI-enabled Raman spectroscopic techniques, as a promising avenue for biosensing applications, have significantly boosted bacteria identification. By converting spectra into images, the dataset is enriched with more detailed information, allowing AI to identify bacterial isolates with enhanced precision. However, previous studies usually suffer from a trade-off between high-resolution spectrograms for high-accuracy identification and short training time for data processing. Here, we present an efficient bacteria identification strategy that combines deep learning models with a spectrogram encoding algorithm based on wavelet packet transform and Gramian angular field techniques. In contrast to the direct analysis of raw Raman spectra, our approach utilizes wavelet packet transform techniques to compress the spectra by a factor of 1/15, while concurrently maintaining state-of-the-art accuracy by amplifying the subtle differences via Gramian angular field techniques. The results demonstrate that our approach can achieve a 99.64 % and a 90.55 % identification accuracy for two types of bacterial isolates and thirty types of bacterial isolates, respectively, while a 90 % reduction in training time compared to the conventional methods. To verify the model's stability, Gaussian noises were superimposed on the testing dataset, showing a specific generalization ability and superior performance. This algorithm has the potential for integration into on-site testing protocols and is readily updatable with new bacterial isolates. This study provides profound insights and contributes to the current understanding of spectroscopy, paving the way for accurate and rapid bacteria identification in diverse applications of environment monitoring, food safety, microbiology, and public health.

Microwave-optical spectroscopy of Rydberg excitons in the ultrastrong driving regime

Abstract We study the ultrastrong driving of Rydberg excitons in Cu$_2$O by a microwave field. The effect of the field is studied using optical absorption spectroscopy, and through the observation of sidebands on the transmitted laser light. A model based on Floquet theory is constructed to study the system beyond the rotating wave approximation. We obtain near quantitative agreement between theory and experiment across a 16-fold range of microwave field strengths spanning from the perturbative to the deep strong driving regime. Compared to Rydberg atoms, the large non-radiative widths of Rydberg excitons leads to new behaviour such as the emergence of an absorption continuum without ionization.

Spectroscopic Evidence of Ultrafast Topological Phase Transition by Light-Driven Strain.

Enabling reversible control over the topological invariants, transitioning them from nontrivial to trivial states, has fundamental implications for quantum information processing and spintronics. It offers a promising avenue for establishing an efficient on/off switch mechanism for robust and dissipationless spin-currents. While mechanical strain has traditionally been advantageous for such manipulation of topological invariants, it often comes with the drawback of in-plane fractures, rendering it unsuitable for high-speed, time-dependent operations. This study employs ultrafast optical and THz spectroscopy to explore topological phase transitions induced by light-driven strain in Bi2Se3. Bi2Se3 requires substantial strain for Z2 switching. Our observations provide experimental evidence of ultrafast switching behavior, demonstrating a transition from a topological insulator with spin-momentum-locked surfaces to hybridized states and normal insulating phases under ambient conditions. Notably, applying light-induced strong out-of-plane strain effectively suppresses surface-bulk coupling, facilitating the differentiation of surface and bulk conductance even at room temperature─significantly surpassing the Debye temperature. We expect various time-dependent sequences of transient hybridization and manipulation of topological invariant through photoexcitation intensity adjustments. The sudden surface and bulk transport alterations near the transition point enable coherent conductance modulation at hypersound frequencies. Our findings on the potential of light-triggered ultrafast switching of topological invariants hold promise for high-speed topological switching and its related applications.

Classification of Vaginal Cleanliness Grades through Surface‐Enhanced Raman Spectral Analysis via The Deep‐Learning Variational Autoencoder–Long Short‐Term Memory Model

In this study, it is aimed to establish a novel method based on a deep‐learning‐guided surface‐enhanced Raman spectroscopy (SERS) technique to achieve rapid and accurate classification of vaginal cleanliness levels. We proposed a variational autoencoder (VAE) approach to enhance spectral quality, coupled with a deep learning algorithm long short‐term memory (LSTM) neural network to analyze SERS spectra produced by vaginal secretions. The performance of various machine learning (ML) algorithms is assessed using multiple evaluation metrics. Finally, the reliability of the optimal model is tested using blind test data (N = 10/group for each cleanliness level). The data quality of the SERS fingerprints of four types of vaginal secretions is significantly improved after VAE decoding and reconstruction. The signal‐to‐noise ratio of the generated spectra increased from the original 2.58–11.13. Among all algorithms, the VAE–LSTM algorithm demonstrates the best prediction ability and time efficiency. Additionally, blind test datasets yielded an overall accuracy of 85%. In this study, it is concluded that the deep‐learning‐guided SERS technique holds significant potential in rapidly distinguishing between different levels of vaginal cleanliness through human vaginal secretion samples. This contributes to the efficient diagnosis of vaginal cleanliness levels in clinical settings.

Effects of Biostimulants on the Eco-Physiological Traits and Fruit Quality of Black Chokeberry (Aronia melanocarpa L.).

Biostimulants contribute to the physiological growth of plants by enhancing the quality characteristics of fruit without harming the environment. In addition, biostimulants applied to plants strengthen nutritional efficiency, abiotic stress tolerance, and fruit biochemical traits. We investigated the effectiveness of specific organic biostimulants. Five treatments were tested: (1) control (H2O, no biostimulants); (2) Magnablue + Keyplex 350 (Mgl + Kpl350); (3) Cropobiolife + Keyplex 120 (Cpl + Kpl120); (4) Keyplex 120 (Kpl120); and (5) Magnablue + Cropobiolife + Keyplex 120 (Mgl + Cpl + Kpl120) on the mineral uptake and physiology in black chokeberry (Aronia) plants, as well as the quality of their berries. The different treatments were applied to three-year-old chokeberry plants, and the experimental process in the field lasted from May to September 2022 until the harvest of ripe fruits. Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) revealed that the fifth treatment significantly increased concentrations of P, Ca, and K. Additionally, the fifth treatment enhanced photochemical efficiency (Fv/Fm), water-splitting efficiency (Fv/Fo) in PSII, and the performance index (PI) of both PSI and PSII in chokeberry leaves. Improvements in photosynthesis, such as CO2 assimilation (A), transpiration (E), and water-use efficiency (A/E), were also noted under biostimulant applications. Upon harvesting the ripe fruits, part of them was placed at room temperature at 25 °C, while the rest were stored at 4 °C, RH 90% for 7 days. The cultivation with biostimulants had beneficial effects on the maintenance of flesh consistency, ascorbic acid concentration, and weight of berries at 4 and 25 °C, especially in the 5th treatment. Moreover, the total antioxidant capacity, anthocyanin concentration, and total phenols of the berries were notably higher in the third and fifth treatments compared to the control. These data suggest that selecting appropriate biostimulants can enhance plant yield and fruit quality by potentially activating secondary metabolite pathways.

Green Pathway of Urea Synthesis Through Plasma–Ice Interaction: Optimization and Mechanistic Insights With N2 + CO2 and NH3 + CO2 Gas Mixtures

ABSTRACTThis study presents a green approach for urea synthesis using plasma–ice interaction with N₂ + CO₂ and NH₃ + CO₂ gas mixtures. Plasma characterization via electrical and optical emission spectroscopy revealed that urea formation involves complex reactions driven by high‐energy species, producing reactive nitrogen and carbon intermediates. Plasma‐treated ice exhibited increased pH, electrical conductivity (EC), and reduced oxidation–reduction potential (ORP). Optimization of parameters, especially treatment time, significantly influenced urea formation. The NH₃ + CO₂ plasma produced a higher urea concentration (7.7 mg L−1) compared to the N₂ + CO₂ plasma (0.55 mg L−1) due to ammonia's greater reactivity. This method offers a sustainable, energy‐efficient alternative to traditional urea synthesis processes.

Iodine‐DMSO Catalyzed β‐carboline Derivative Chemoselective Fluorescent Molecule for Detection of Fluoride and Cyanide Anion

AbstractIn this work, a simple and highly sensitive fluorescent receptor 1‐(p‐tolyl)‐4,9‐dihydro‐3H‐pyrido[3,4‐b]indole (receptor 1), has been synthesized for the detection of fluoride and cyanide ions. The receptor 1 was very selective towards fluoride and cyanide anions over other anions such as Cl−, Br−, I−, HSO4−, NO3−, HPO42−, and OAc−. Synthesized receptor 1 shows low detection limits 3.29 × 10−8 M for fluoride ion, which was determined by UV–vis absorption. While LOD for cyanide was observed to be 61.5 nM. Importantly, while, receptor 1 produced binding constant for fluoride and cyanide in DMSO‐H2O (1:9, v/v) was found to be 5 × 10−4M−1 and 3.28 × 10−5 M−1, respectively. The variation in the optical and fluorescence spectroscopy confirms the formation of N─H⋯F− and N─H⋯CN− hydrogen bond of the pyrrolic proton The sensing mechanism displayed by receptor 1 for fluoride and cyanide ions was confirmed by 1H‐NMR and FT‐IR spectroscopic along with density functional theory (DFT) calculations. Solution with the fluoride and cyanide ions in DMSO‐H2O (1:9, v/v) shows a color change from light yellow to blue under UV light and yellow to colorless in visible light.

Boosted photoelectrochemical activity of anodic titanium dioxide nanotubes by electrophoretically decorated nickel–aluminum layered double hydroxide

The splitting of water using photoelectrochemical (PEC) processes is a promising method for generating renewable hydrogen. However, the practical efficiency of converting solar energy to fuel in PEC systems remains limited by inadequate light absorption and the swift recombination of photogenerated charge carriers within the photoelectrode material. In our research, we present a photoanode that addresses these challenges. In this work, we report on the electrophoretic deposition of nickel–aluminum layered double hydroxide (NiAl-LDH) under different voltages onto anodic TiO2 nanotube arrays (TNTAs) as a convenient and economically viable fabrication route of versatile and durable photoanodes. PEC and optical spectroscopy examinations, including linear sweep voltammetry, electrochemical impedance spectroscopy, Mott-Schottky plots, and diffuse reflectance spectroscopy, reveal that NiAl-LDH/TNTAs composites exhibit a superior enhancement of visible light absorption and consequently water splitting photocurrent. We interpret the improvement of PEC water splitting performance of the NiAl-LDH/TNTAs with the band structure speculated by the binding energy and the flat-band potential and band-gap measurements which features this nanocomposite as a novel photocatalyst for future hydrogen energy applications.