Saponite is a trioctahedral 2:1 smectite with the ideal composition MxMg3AlxSi4-xO10(OH,F)2.nH2O (M = interlayer cation). Both the success of the saponite synthesis and the determination of its applications depends on robust knowledge of the structure and composition of saponite. Among the routine characterization techniques spectroscopic methods are the most common. This review, thus, provides an overview of various spectroscopic methods to characterize natural and synthetic saponite with focus on the extensive work by one of the authors (JTK). The IR and Raman spectra of natural and synthetic saponites are discussed in detail including the assignment of the observed bands. The crystallization of saponite is discussed based on the changes in the IR and Raman spectra and a possible crystallization model is provided. Infrared emission spectroscopy has been used to study the thermal changes of saponite in-situ including the dehydration and (partial) dehydroxylation up to 750˚C. 27Al and 29Si Magic-Angle-Spinning Nuclear Magnetic Resonance Spectroscopy is discussed (as well as 11B and 71Ga for B- and Ga-Si substitution) with respect to, in particular, Al(IV)/Al(VI) and Si/Al(IV) ratios. X-ray Photoelectron Spectroscopy provides besides chemical information also some information related to the local environments of the different elements in the saponite structure as reflected by their binding energies.

Nanotechnology has enabled the development of novel therapeutic strategies such as targeted nanodrug delivery systems, control and stimulus-responsive release mechanisms, and the production of theranostic agents. As a prerequisite for the use of nanoparticles as drug delivery systems, the amount of loaded drug must be precisely quantified, a task for which two approaches are currently used. However, both approaches suffer from the inefficiencies of drug extraction and of the solid-liquid separation process, as well as from dilution errors. This work describes a new, reliable, and simple method for direct drug quantification in polymeric nanoparticles using attenuated total reflection Fourier transform infrared spectroscopy, which can be adapted for a wide variety of drug delivery systems. Silk fibroin nanoparticles and naringenin were used as model polymeric nanoparticle carrier and drug, respectively. The specificity, linearity, detection limit, precision and accuracy of the spectroscopic approach were determined in order to validate the method. A good linear relation was observed within 0.00 to 7.89 % of naringenin relative mass with an R2 of 0.973. The accuracy was determined by the spike and recovery method. Results showed an average 104% recovery. The limit of detection and limit of quantification of the drug loading content were determined to be 0.3 and 1.0 %, respectively. The method's robustness is demonstrated by the notable similarities between the calibrations carried out in two different equipment and institutions.

The Chang’e-4 (CE-4) lunar rover, equipped with The Visible and Near-IR Imaging Spectrometer(VNIS) which based on acousto-optic tunable filter spectroscopy, was launched to the far side of the moon on December 8, 2018. The detection band of VNIS ranges from 0.45 to 2.4μm. Because of the weak reflection of infrared radiation from the lunar surface, a static electronic phase-locked acquisition method is adopted in the infrared channel for signal amplification. In this paper, full-link simulations and modeling are conducted of the infrared channel information flow of the instrument. The signal/noise characteristics of VNIS are analyzed in depth, and the signal-to-noise(SNR) ratio prediction and laboratory verification are presented. On January 4, 2019, the VNIS started working successfully and acquired high-resolution spectrum data of the far side of the moon for the first time. Through analysis, the SNR ratio is in line with predictions, and the data obtained by VNIS in orbit are consistent with the information model proposed in this paper.

The present study evaluates the bioremediation potential of indigenous bacterial species isolated from dye-contaminated soil samples from small dyeing outlet located in Ilorin. The water pollution index was estimated based on the physicochemical characteristics and heavy metal concentrations of the raw (Day 0) and treated textile wastewater such as pH, biochemical oxygen demand-5, chemical oxygen demand, total suspended solids and total dissolved solid with mean values of 8.85±0.45 mg/L, 1200±21.3 mg/L, 2440±31.3 mg/L, 1660±17.2 mg/L and 2650±28.1 mg/L respectively, similarly, Lead was the most abundant heavy metal detected in the sample while Cadmium concentration was the lowest with the mean values of 3.52±0.00 mg/L and 2.18±0.00 mg/L respectively. The bacterial strain with highest dye decolorization capacity was screened and identified as Bacillus licheniformis ZUL012.The isolate was consequently used for the bioremediation of the wastewater over a period of 10 days. The results showed an incredible reduction in the physiochemical characteristics and heavy metal concentrations of the textile wastewater in the following ranges (8.85-6.55), (1200-300) mg/L, (2440-518) mg/L, (1660-666) mg/L and (2650-920) mg/L with the highest removal efficiency of 75 %, 78 %, 60%, 65%, recorded for biochemical oxygen demand, chemical oxygen demand, total suspended solid, total dissolved solid, respectively while that of heavy metals such as lead, cadmium, chromium and nickel were 80 %, 60 %, 67 %, 72 % reduction, respectively. Laccase and Azoreductase activities tend to decrease as the pH gradually moved towards acidic condition during the bioremediation process. Toxicity of the treated effluent was assessed using Maize and Bean seed germination test. Conclusively, these research findings can serve as a framework for the outlet design of wastewater treatment plant for local textile outlets.

2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ) is a molecule widely employed as a very effective p-dopant of semi-conducting polymers, such as poly(3-hexylthiophene -2,5-diyl) (P3HT). The CN stretching transitions of F4TCNQ are exceptionally sensitive to the charge state of the molecule, thus allowing the doping diagnosis via IR spectroscopy. Less pronounced frequency shifts can reveal characteristics of the intermolecular environment. We present a systematic study based on Density Functional Theory (DFT) calculations and on experiments aimed at exploring how different factors, such as the charge state and the environment, modify the vibrational spectra of F4TCNQ. While several effects on the vibrational frequencies are well known and have been thoroughly investigated in the past, this study focuses on the infrared intensities of the CN stretching modes and reveals that they are strongly affected both by the charge state of the molecule and by the surrounding medium: it is then mandatory to consider such remarkable intensity modulation for any quantitative diagnosis based on spectroscopic measurements, e.g., concerning the amount of F4TCNQ molecules involved in the formation of charge transfer complexes.

Re-purposing wastewater is necessary to circumvent water shortage for vegetable farmers in urban areas and to reduce public health hazards associated with using contaminated water for irrigation. In this study, the efficiency of biochar filtration systems in removing contaminants from wastewater was investigated. The hydraulic retention time (HRT) and the cleaning mechanism of coconut biochar filtration systems were examined. The filtration systems were designed with biowastes, specifically coconut biochar, as an adsorbent to remove pollutants from wastewater. Two setups of bed, 40 cm and 30 cm thick, were examined under different HRTs. Fourier Transform Infrared Spectroscopy (FTIR) analysis and Scanning Electron Microscopy (SEM) imaging were used to ascertain the properties of the biochar that enhances the removal of pollutants in wastewater. For both setups, the removal of heavy metals (Lead, Pb and copper, Cu) as well as Total Coliform (TC) was judged satisfactory but appeared ineffective in removing E. coli.

In this study, the biodegradation properties of leather treated with various finishing chemicals were evaluated in order to enhance the sustainability of leather processing. We applied advanced analytical techniques including FT-IR, thermogravimetric analysis (TGA), and solid-state NMR spectroscopy. Leather samples treated with different polymers, resins, bio-based materials, and traditional finishing agents were subjected to a composting process under controlled conditions to measure their biodegradability. The findings revealed that bio-based polyurethane finishes and acrylic wax exhibited biodegradability, while traditional chemical finishes like isocyanate and nitrocellulose lacquer showed moderate biodegradation levels. The results indicated significant differences in the biodegradation rates and the impact on plant germination and growth. Some materials, such as black pigment, nitrocellulose lacquer and wax, were beneficial for plant growth, while others, such as polyurethane materials, had adverse effects. These results support the use of eco-friendly finishes to reduce the environmental footprint of leather production. Overall, this study underscores the importance of selecting sustainable finishing chemicals to promote eco-friendly leather manufacturing practices.

Buildings are rapidly becoming more digitized, to a large extent due to developments in IoT. This will provide many opportunities but also a number of challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor the building’s status effectively. This monitoring is essential for various services that rely on information about the presence and activities of individuals within different areas of the building. Occupancy information, including people counting, occupancy detection, location tracking, and activity detection, plays a vital role in the management of smart buildings. In this article, our primary focus is on the use of Passive Infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to many others. Despite numerous literature review articles in the field of occupancy information, there currently isn’t a specialized literature review dedicated to occupancy information derived specifically from PIR sensors.Therefore, this study analyzes articles that specifically explore the application of PIR sensors for occupancy information. This article provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, with a focus on applications in people counting, activity detection, and localization (traking and location). It consolidates findings from a number of articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information.These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects.These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The paper seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

The Advanced Meteorological Image (AMI) onboard GEOKOMPSAT 2A (GK-2A) enables the retrieval of dust aerosol optical depth (DAOD) from geostationary satellites using infrared (IR) channels. IR observations allow the retrieval of DAOD and the dust layer altitude (24 hour) over surface properties, particularly over deserts. In this study, dust events in northeast Asia from 2020 to 2021 were investigated using five GK-2A thermal IR bands (8.7, 10.5, 11.4, 12.3, and 13.3 μm). For the dust cloud, the observed brightness temperature differences (BTDs) of 10.5 and 12.3 μm were found to be always negative, while the BTD of 8.7 and 10.5 μm varies relying on the dust intensity. Making the best use of optical properties, a physical approach for DAOD lookup tables (LUTs) was developed using IR channels to retrieve the DAOD. This study simulated the characteristics of thermal radiation transfer using the forward model and dust aerosols can explain by BTD (10.5, 12.3 μm), which is an intrinsic characteristic of dust aerosol. The DAOD and property of dust can be gained from the brightness temperature (BT) of 10.5 μm and the BTD of 10.5, 12.3 μm. Additionally, the cumulative distribution function (CDF) was employed to strengthen the continuity of 24-hour DAOD, which was applied to the algorithm by calculating the conversion value coefficient for the DAOD error correction of the IR with daytime visible aerosol optical depth (VAOD) as the true value. The results show that DAOD product can be successfully used during both daytime and nighttime to continuously monitor the flow of yellow dust from the GK-2A satellite in northeast Asia. In particular, the validation result of IR DAOD was similar to that of the active satellite product (CALIPSO/CALIOP), which showed a tendency similar to that for IR DAOD at night.

Since the photocatalytic reaction is a surface reaction, enhancing the gas movement around photocatalyst would promote photocatalytic CO₂ reduction performance. A new approach to enhance the gas movement around the photocatalyst by the natural thermosiphon movement of gasses around photocatalyst using black body material was proposed and confirmed experimentally, but the heat transfer mechanism on the phenomena was not clarified yet. The purpose of this study is to clarify the heat transfer mechanism of it. This study calculated the temperature of mixed gases of CO₂ and NH₃ around P₄O₁₀/TiO₂ photocatalyst by the heat transfer formula. It is revealed that there was no difference between the increase temperature (T_g) from the temperature at the beginning of the CO₂ reduction experiment (T_ini) and the temperature of mixed gases of CO₂ and NH₃ measured by thermocouple in the experiment (T_e) under the illumination condition with a visible light (VIS) + an infrared light (IR) and IR only. The heat transfer model proposed by this study has predicted T_g well under the illumination condition with VIS + IR and IR only well. On the other hand, it is revealed that the difference between T_g and T_e was as large as 10 ℃ under the illumination condition with an ultra violet light (UV) + VIS + IR.

The extensive use of engineered nanoparticles (ENPs) such as zinc oxide nanoparticles (ZnO NPs) in various commercial fields has spurred significant concern about its possible toxicological effects of ZnO NPs on human health and eco-system. Therefore, it is substantial to understand the aggregation phenomena which could attenuate this effect. This data article provides information on aggregation of ZnO NPs under the influence of various parameters of water chemistry. Moreover, Taguchi orthogonal array L27(313) design matrix was used to evaluate the effect of multiple parameters in aqueous solution. The data were obtained from single beam UV-Vis spectrophotometer (Optizen 2120 UV, Mecasys, Korea) using Optizenview 4.2.5 PC interference software, Fourier transform infrared spectrometer (FT/IR-4700, spectroscopy, JASCO Analytical Instruments, Easton, USA) and particle electrophoresis (NanoZS, Zetasizer, Malvern Instruments Ltd, Worcestershire, UK). The dataset draws attention on dominant parameters influencing aggregation of ZnO NPs in water. The analysis of variance (ANOVA) data revealed that electrolyte concentration, type and natural organic matter (NOM) concentration were the most significant parameters. Furthermore, FT-IR data presents a possible mechanism of ZnO NPs stabilization in the presence of different NOM. This data will be helpful for the development of environmental risk assessment strategies and prediction of fate and mobility of other ENPs in the aquatic environment.

There have been multiple technological advancements that promise to gradually enable devices to measure and record signals with high resolution and accuracy in the domain of Brain Computer Interfaces (BCI). Multi-modal BCIs have been able to gain significant traction given the potential to enhance signal processing by integrating different recording modalities. In this review, we explore the integration of multiple neuroimaging and neurophysiological modalities including Electroencephalography (EEG), Magnetoencephalography (MEG), Functional Magnetic Resonance Imaging (fMRI), Electrocorticography (ECoG), and Single-Unit Activity (SUA). This multimodal approach leverages the high temporal resolution of EEG and MEG with the spatial precision of fMRI, the invasive yet precise nature of ECoG, and the single-neuron specificity provided by SUA. The paper highlights the advantages of integrating multiple modalities, such as increased accuracy and reliability, and discusses the challenges and limitations of multimodal integration. Furthermore, we explain the data acquisition approaches for each of these modalities. We also demonstrate various software programs that help in extracting, cleaning, and refining the data. We conclude this paper with discussion on the available literature highlighting recent advances, challenges, and future directions for each of these modalities.

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioural as-sessments, enabling a comprehensive evaluation of the hens' health, behaviour, and population counts. To address the issue of insufficient data samples in the health detection process for indi-vidual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environ-ments, various data enhancement methods such as rotation, shearing, colour enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for ap-plying visible light images and corresponding thermal infrared images in the health detection, behavioural analysis, and counting of caged laying hens under large-scale farming.

This research proposes a near-infrared hyperspectral imaging/two-branch convolutional neural network (NIR-HSI/2B-CNN) algorithmic scheme to detect mango anthracnose of the species Colletotrichum asianum at the early stages of disease development. In the algorithmic model development, root mean square propagation was used as the solver to train the neural network, given 150 epochs. In addition, spectral raw data was preprocessed to transform it into an understandable and efficient format. The optimal classification model was the 2B-CNN model with 1st-derivative preprocessing, achieving an accuracy of 0.94 for the calibration set and 0.71 for the prediction set. The proposed NIR-HSI/2B-CNN scheme could detect anthracnose mangoes since the the first day of inoculation of the spore suspension (i.e., day 0) through to day 3, achieving a moderate classification accuracy. Meanwhile, the accuracy of conventional convolutional neural networks (CNN) were within a range of 0.66-0.67 for the calibration set and 0.55-0.57 for the prediction set. The results indicated that incorporating spatial features in the 2B-CNN modeling enhanced the prediction performance of the algorithm. The proposed NIR-HSI/2B-CNN algorithmic scheme needs refinements to be able to reliably sort mango fruits into those suitable for premium fresh consumption and export without anthracnose and those for domestic consumption or processing. The novelty of this research lies in the use of NIR-HSI and 2B-CNN algorithm to detect plant pathogens at the early stages of disease development. In addition, the new method of natural simulation to deposit the fungal spores onto the mango surface by spraying spore suspension onto the mango surface where the conidia penetrated unaided into the underneath of the mango peel is proposed.

The effect of sonication on the molecular characteristics of poliacrylic acid (Carbopol® Ultrez 10) and its rheological behavior in aqueous dispersions and microgels containing 0.25 wt. % of the polymer was analyzed in this work by rheometry, weight-average molecular weight (Mw) measurements via static light scattering (SLS), Fourier transform infrared (FTIR) spectroscopy and confocal microscopy. For this, the precursor dispersion and the microgels were sonicated in a commercial ultrasound bath at constant power and different times. We observed a softening of the microgel microstructure consisting of a systematic decrease in its shear modulus, yield stress and viscosity with increasing sonication time, while their overall Herschel-Bulkley (H-B) behavior was preserved. SLS measurements evidenced a reduction of Mw of polyacrylic acid with sonication time. Separately, FTIR measurements indicate that sonication produces scission in the C-C links of the Carbopol® backbone, which results in chains with the same chemistry but lower molecular weight. Finally, confocal microscopy measurements revealed a concomitant diminution of the size of the microsponge domains with sonication time, which is reflected in a softer microstructure resulting from reduction of the molecular weight of polyacrylic acid. The present results indicate that both the microstructure and the rheological behavior of Carbopol® microgels, in particular, and complex fluids in general, may be manipulated or tailored by high-power ultrasonication.

Knowing, with reasonable accuracy, the dry matter (DM) content of Hass avocado fruit will help determine when the fruit must be harvested. The reliability of predictive models based on near infrared spectra for DM quantification depends on the ability of the spectra to be representative of the DM gradient within a whole fruit. The aim of this work was to develop a methodology to determine the optimum number of spectra to develop a robust model for DM content quantification. Three spectra were recorded for each zone of the intact fruit: peduncle, equator, and base. Each scanning point was sampled, and the DM content was determined using oven drying. Two-way ANOVA confirmed the DM gradient within the whole fruit. This gradient was observed within spectra using the RMS (root mean square) criterion and PCA. The PLS models showed that at least one spectrum per zone could be enough to construct an efficient and robust model for dry matter quantification.

The boreal Hemisphere has been experiencing increasing extreme hot and dry conditions over the past few decades, consistent with anthropogenic climate change expectations. The continental extension of the phenomenon calls for tools and techniques capable of monitoring the global to regional scales. In this context, the satellite is the only system that can satisfy the need for global coverage. The main objective we have addressed in the present paper is the capability of infrared satellite observations helping to monitor the vegetation stress due to increasing drought and heatwaves in summer. We have designed and implemented a new water deficit index (wdi) that exploits satellite observations in the infrared to retrieve humidity, air temperature, and surface temperature simultaneously. These three parameters are combined to provide the water deficit index. The index has been developed based on the Infrared Atmospheric Sounder Interferometer or IASI, which covers the infrared spectral range 645 to 2760 cm-1 with a sampling of 0.25 cm-1. The index has been used to study the 2017 heatwave, which hit Continental Europe from May to October. In particular, we have examined Southern Italy, where Mediterranean forests suffer from climate change. We have computed the index's time series and show that it can be used to indicate the atmospheric background conditions associated with meteorological drought. We have also found a good agreement with soil moisture, which suggests that the persistence of anomalously high water deficit index was an essential driver of the rapid development and evolution of the exceptionally severe 2017 droughts.

Breast cancer is the most commonly diagnosed cancer in women in both developed and developing countries. Due to the increasing alarms in the detection of cases every year, it is considered a priority to evaluate new molecules as potential antitumoral agents. Among these molecules, cationic peptides have emerged as promising therapeutic agents with a membrane-based mechanism of action very hard to counteract by the cancer cells. This biophysical study seeks to offer preliminary insights into how NA-CATH:ATRA-1-ATRA-1, a synthetic cationic peptide derived of the peptide NA-CATH, isolated from the species Natra aja or Chinese cobra, is a potential antitumoral compound at the membrane level on breast cancer cell lines. The interaction of the NA-CATH-ATRA-1-ATRA-1 with two lipid systems representative of tumoral and non-tumoral membranes was followed by differential scanning calorimetry and Fourier-transform infrared spectroscopy. The results showed a strong interaction of NA-CATH-ATRA-1-ATRA-1 with the lipids present in the eukaryotic membranes, especially phosphatidylserine, suggesting a mechanism based on the electrostatic interaction of the peptide with the anionic lipids of the tumoral cancer membranes. The biophysical studies are essential to understand the mechanism of action of a potential pharmaceutics.

We report a novel dual-band barrier infrared detector (DBIRD) design using type-II superlattices (T2SLs). The DBIRD structure consists of back-to-back barrier diodes: a "blue channel" (BC) diode which has an nBp architecture, an n-type layer of larger bandgap for absorbing blue band infrared/barrier/p-type layer, and a "red channel" (RC) diode which has an pBn architecture, a p-type layer of smaller bandgap for absorbing red band infrared/barrier/n-type layer. Each has a unipolar barrier using a T2SL lattice matched to a GaSb substrate to impede the flow of majority carriers from the absorbing layer. Each channel in the DBIRD can be independently accessed with a low bias voltage, as is preferable for high-speed thermal imaging. Device modeling of DBIRDs and simulation results of the current-voltage characteristics under dark and illuminated conditions are also presented. They predict that dual-band operation of the DBIRD will produce low dark currents and 27-33% quantum efficiencies for the in-band photons in the BC with λc=5.58 μm, and a nearly-constant 33% in the RC with λc=8.05 μm. The spectral quantum efficiency of the BC for 500 K blackbody radiation is approximately 20% over the range of λ=3 − 4.2 μm, while that of the RC has a peak of 42% at 5.7 μm. The DBIRD may provide improved high-speed dual-band imaging in comparison with NBn dual-band detectors.

Reactive oxygen species (ROS) play a pivotal role in cellular function, and their overproduction contributes to oxidative stress, fatigue, aging, and diseases. To counteract these effects, incorpo-rating antioxidant-rich nutrients is recommended. Functional waters, including electrolyzed hydrogen water (EHW), are gaining attention for potential antioxidant effects through daily in-take. This study employs aquaphotomics and near-infrared spectroscopy (NIRS) to elucidate the molecular mechanisms behind the antioxidant activity of EHW. Previous investigation reported a 100% loss in antioxidant functionality through autoclaving, and a 40% decrease with hydrogen gas removal, prompting the current investigation into the mechanisms of antioxidant effects and especially the role of water molecular structure in it. Aquaphotomics analysis of near infrared (NIR) spectral data revealed that the primary characteristic of EHW associated with anti-radical function is presence of water solvation shells. Autoclaving and degassing led to loss of these water molecular species and a significant increase in free water molecules. The study suggests that water shells (solvation shells) in EHW play a crucial role in neutralizing ROS, although the specific ions or nanoparticles responsible for creating these shells remain unidentified. This concept aligns well with existing proposals of the crucial role of water solvation shells in ROS neutralization.

Background: Glioblastoma remains a challenging brain tumor to treat due to its infiltrative nature. Accurately identifying tumor boundaries during surgery is crucial for successful tumor resection. This study introduces an innovative intraoperative visualization method utilizing a surgical fluorescence microscope to precisely locate tumor cells. The focus of the study is on IR-Glint, a novel preparation comprising Cy7.5-labeled aptamers Gli-233nt and Gli-55_3L specific to human glioblastoma. Methods: The aptamers were modified using molecular modeling and quantum chemical techniques. The effectiveness of the preparation was assessed using flow cytometry and microscopy on primary cultures of human glioblastoma. In vivo studies were conducted on mouse and rabbit models, employing orthotopic xenotransplantation of human brain glioblastoma with various imaging techniques, including PET/CT, in vivo fluorescence visualization, confocal laser scanning, and surgical microscopy. Results: The experiments validated the potential of IR-Glint for intraoperative visualization of glioblastoma using infrared imaging. Surface application of the aptamer-based formulation on the brain allowed clear visualization of the tumor, aiding surgeons in tumor resection. This approach also reduces the dosage required and mitigates potential toxic effects on the patient. Conclusions: This study demonstrates the promising potential of using in-frared dye-labeled aptamers for intraoperative visualization of glioblastoma. By improving surgical treatment outcomes in neurosurgery, this novel approach may pave the way for future advancements in the field.

The structural evolution and phase composition of multi-block thermoplastic polyurethanes, with different nature of the soft segments based on poly(butylene adipate) (PBA), were analyzed during in-situ thermal treatments. A combination of synchrotron small- and wide-angle X-ray scattering, differential scanning calorimetry, thermogravimetric analysis, and FTIR spectroscopy was used to determine the influence of macrodiol nature and crystallization conditions on the polymorphic behavior of PBA. Using a new synthesis scheme, a relatively high degree of crystallinity for urethane blocks was achieved, which depends on the diisocyanate type in the structure of the soft segment. The hard segment domains impose geometrical constraints on PBA, thereby altering its crystallization process compared to the neat oligomer. Thus, crystallization after annealing at a low temperature (80 °C) is fast, predominantly yielding a metastable β-phase. When heated to 180 °C, which is higher than the hard-segment melting temperature, a phase-separated structure is observed. Subsequent crystallization is slower, favoring the formation of the stable α-PBA modification. The phase separation can be observed even after the hard block melting. A notably slow crystallization from an isotropic melt was documented after the disruption of phase separation at 230 °C.

A recent study demonstrated that noninvasive measurements of cortical hemodynamics and metabolism in the resting human prefrontal cortex can facilitate quantitative metrics of unilateral mitochondrial-hemodynamic coupling and bilateral connectivity in infraslow oscillation (ISO) frequencies in young adults (YA). The ISO includes three distinct vasomotions with endogenic (E), neurogenic (N), and myogenic (M) frequency bands. The goal of this study was to prove the hypothesis that there are significant differences between young and older adults (OA) in the unilateral coupling (uCOP) and bilateral connectivity (bCON) in the prefrontal cortex. Accordingly, we performed measurements from 24 OA (67.2 ± 5.9 years of age) using the same 2-channel broadband near-infrared spectroscopy (bbNIRS) setup and resting-state experimental protocol as those in the recent study (Shahdadian et al, Cerebral Cortex Communications, 2022). After quantification of uCOP and bCON in three E/N/M frequencies and statistical analysis, we demonstrated that OA had significantly weaker bilateral hemodynamic connectivity but significantly stronger bilateral metabolic connectivity than YA in the M band. Furthermore, OA exhibited significantly stronger unilateral coupling on both prefrontal sides in all E/N/M bands, particularly with a very large effect size in the M band (> 1.9). These age-related results clearly support our hypothesis and were well interpreted following neurophysiological principles. The key finding of this paper is that the neurophysiological metrics of uCOP and bCON are highly associated with age and may have the potential to become meaningful features for human brain health and be translatable for future clinical applications, such as early detection of Alzheimer’s disease.

In China, the production of crop straw has been estimated to be approximately 800 Million tons yearly of which about 40% was burned. Corn stover is one of the main agricultural wastes in China. It has shown that lignin in corn stover could be effectively removed byMyrothecium verrucaria. The effects of the pretreatment of corn strover by Myrothecium verrucaria on compost were studied. The results showed that corn stover pretreatment by Myrothecium verrucaria, the Cellulose, Hemicellulose, and lignin were degraded and the results were 33.43%, 11.53% and 18.70% respectively. Scanning electron microscope (SEM) analysis showed that the surface structure of corn stover was changed. Fourier transform infrared spectroscopy (FTIR) analysis showed that the degradation products of lignin were increased. The exposed area of cellulose and hemicellulose was increased. Compared with the control group, the pH value was stable and the temperature was higher. The content of nitrogen in the material decreased, while the contents of total phosphorus and total potassium increased.The C/N ratio of materials decreased after composting.The results showed that the pretreatment of Myrothecium verrucaria improve the degradation of lignocelluloses, a great contribution was made to reduce the causes loss of plant nutrient and to fight against environmental pollution.

Recently Candida auris has emerged as a multi-resistant fungal pathogen, with a significant clinical impact, and has become a critical issue in WHO fungal priority list, due to its unique ability to persist for long time on human skin and hospital environments. It is therefore fundamental to reinforce hospital surveillance protocols to limit nosocomial outbreaks. The study purpose was the Fourier-Transform Infrared Spectroscopy (FT-IR) application to investigate the phylogenetic relationships between isolated strains within the C. auris outbreak at the university Intensive Care Unit of a Tertiary University hospital in Turin (Italy). In order to calculate a clustering cut-off, intra- and inter-isolates distance values were analysed. Data showed the presence of a major cluster Alfa and a minor cluster Beta with a defined C. auris clustering cut-off. The results were validated by an external C. auris strain and Principal Component and Linear Discriminant Analysis. The application of FT-IR technology had allowed to obtain important information about the phylogenetic relationships between the analysed strains, defining for the first time a “not WGS-based” clustering cut-off with a statistical-mathematical approach. FT-IR could represent a valid alternative to molecular methods for rapid and cost saving typing of C. auris strains with important clinical implications.

In this paper I will address Dr. Sonne’s questions about will, agency, choice, consciousness, relevant brain regions, impacts of disorders and their therapeutics, and I will do this by referring to my theory, Dual-brain Psychology, which posits that within most of us there exist two mental agencies with different experiences, wills, choices, and behaviors. Each of these agencies is associated as a trait with one brain hemisphere (either left or right) and its composite regions. One of these agencies is more adversely affected by past traumas and is more immature and more symptomatic while the other is more mature and healthier. The theory has extensive experimental support through 17 peer-reviewed publications with clinical and non-clinical research. I will discuss how this theory relates to the questions that Dr. Sonne presented and will discuss also my published theory on the physical nature of subjective experience and its relation to the brain and how that theory interacts with DBP, and how the 2 theories relate to subjective experience, will, behavior, psychopathology and its treatment.

The simplified, combined protocol admits children with a mid-upper-arm circumference (MUAC) of <125 mm or edema to malnutrition treatment with ready-to-use therapeutic food (RUTF) prescribing 2 daily RUTF sachets to children with MUAC <115 mm or edema and 1 daily sachet to those with MUAC ≥115 mm but <125 mm. This treatment has previously been shown to result in non-inferior programmatic outcomes compared to standard treatment. We aimed at observing its effectiveness in a routine setting at scale, including via delivery by community health workers (CHW). A total of 27 601 children were admitted to the simplified, combined treatment. Treatment resulted in 96% overall recovery with a mean LOS of 40 days and a mean RUTF consumption of 63 sachets per child treated. Among children admitted with MUAC <115 mm or edema 94% recovered with a mean LOS of 55 days and consuming an average of 97 RUTF sachets. Recovery in all sub-groups studied exceeded 90%.Treatment by CHWs resulted in similar (96%) recovery as treatment by formal health care workers (96%). The simplified, combined protocol results in high recovery and low RUTF consumption per child treated, and can safely be adopted by CHWs to provide treatment in the community-level.

Loess provides a valuable terrestrial record of past environmental conditions, including the dynamics of air mass circulation responsible for dust transport. Here we explore variations in the luminescence characteristics of sedimentary quartz and feldspar – dominant minerals in loess-palaeosol sequences (LPS) - as possible tools for identifying changes in source. We investigate luminescence sensitivity, a rapidly measurable index which is the product of interplay between source lithology and the history of the mineral in question. Variations in sensitivity down profile may therefore reflect, among other factors such as pedogenesis and reworking, changes in sediment provenance. We undertake an empirical investigation of the luminescence sensitivity of quartz and feldspar from different grain-size fractions from the climatically sensitive Schwalbenberg LPS in the German Rhine valley, comparing samples from a 30 m core spanning the last full glacial cycle with samples of oxygen isotope stage (OIS) 3-2 age exposed within a c. 6 m profile downslope. The temporal overlap enables comparison of luminescence characteristics with respect to possible provenance change during that timeframe. We find an overall inverse relationship between quartz and feldspar sensitivity, as well as variability in sensitivity between different quartz grain sizes. There is some indication that feldspar sensitivity increases during periods of soil formation down the core. In particular, measurements of IR50 sensitivity on unprocessed sediments show correlation with down-profile trends in more established indicators of provenance. This suggests it may be used to provide a reliable, rapid scan of sensitivity changes, and may suggest source variability over millennial timescales.

High average power narrow-linewidth tunable solid-state lasers in the wavelength region between 2 to 3 μm are attractive light sources for many applications. This paper reports a narrow-linewidth widely-tunable laser system based on the polycrystalline Cr2+:ZnSe elements pumped by the repetitively-pulsed 2.1 µm Ho3+:YAG laser operating at the tens of kilohertz pulse rate. An advanced procedure of ZnSe-element doping and surface improvement was applied to increase the laser-induced damage threshold, which resulted in an increase of the output power of the Cr2+:ZnSe laser system. The high-average-power laser system comprised double master-oscillators and power amplifiers: Ho3+:YAG and Cr2+:ZnSe laser oscillators, and Ho3+:YAG and Cr2+:ZnSe power amplifiers. The output wavelength was widely tuned within 2.3-2.7 µm by an acousto-optical tunable filter inside the Cr2+:ZnSe master oscillator cavity. The narrow-linewidth operation at the pulse repetition rate of 20-40 kHz in a high-quality beam with the average output power up to 9.7 W was demonstrated.

The present study investigates the relationship between the local structure, photocatalytic ability, and cathode performances in sodium-ion batteries (SIBs) and lithium-ion batteries (LIBs) using Ni-substituted goethite nanoparticles (Ni_xFe_1-xOOH NPs) with a range of 'x' values from 0 to 0.5. The structural characterization was performed applying various techniques, including X-ray diffractometry (XRD), Thermogravimetry differential thermal analysis (TG-DTA), Fourier Transform Infrared Spectroscopy (FT-IR), X-ray absorption spectroscopy (XANES/EXAFS), both measured at room temperature (RT), and ⁵⁷Fe Mössbauer spectroscopy recorded at RT and at low temperatures (LT) from 20 K to 300 K, Brunauer-Emmett-Teller surface area measurement (BET), and diffuse reflectance spectroscopy (DRS). In addition, the electrical properties of Ni_xFe_1-xOOH NPs were evaluated by impedance spectroscopy. XRD showed the presence of goethite as the only crystalline phase in prepared samples with x ≤ 0.20, and goethite and α-Ni(OH)₂ in the samples with x > 0.20. Sample with x = 0.10 (Ni10) showed the highest photo-Fenton ability with a first-order rate constant value (k) of 15.8×10^-3 min^-1. The ⁵⁷Fe Mössbauer spectrum of Ni0, measured at RT, displayed a sextet corresponding to goethite, with an isomer shift (δ) of 0.36 mm s^-1 and a hyperfine magnetic distribution (B_hf) of 32.95 T. Moreover, the DC conductivity decreased from 5.52×10^-10 to 5.30×10^-12 (Ω.cm)^–1 with 'x' increasing from 0.10 to 0.50. Ni20 showed the highest initial discharge capacity of 223 mAh g^-1, attributed to its largest specific surface area of 174.0 m² g^-1. In conclusion, Ni_xFe_1-xOOH NPs can be effectively utilized as visible-light-activated catalysts and active cathode materials in secondary batteries.

An effective process monitoring strategy is a requirement for meeting the challenges posed by increasingly complex products and manufacturing processes. To address these needs, this study investigates a comprehensive scheme based on classical machine learning methods, deep learning algorithms, and feature extraction and selection techniques. In a first step, a novel deep learning architecture based on convolutional neural networks (CNN) and gated recurrent units (GRU) is introduced to predict the local weld quality based on mid-wave infrared (MWIR) and near-infrared (NIR) image data. The developed technology is used to discover critical welding defects including lack of fusion (false friends), sagging and lack of penetration, and geometric deviations of the weld seam. Additional work is conducted to investigate the significance of various geometrical, statistical, and spatio-temporal features extracted from the keyhole and weld pool regions. Furthermore, the performance of the proposed deep learning architecture is compared to that of classical supervised machine learning algorithms, such as multi-layer perceptron (MLP), logistic regression (LogReg), support vector machines (SVM), decision trees (DT), random forest (RF) and k-Nearest Neighbors (kNN). Optimal hyperparameters for each algorithm are determined by an extensive grid search. Ultimately, the three best classification models are combined into an ensemble classifier that yields the highest detection rates and achieves the most robust estimation of welding defects among all classifiers studied, which is validated on previously unknown welding trials.

The structure and physicochemical properties of polyvinyl alcohol (PVA) and PVA/glycerol films have been investigated by Fourier-transform infrared spectroscopy (FT-IR), X-ray Diffraction (XRD), thermogravimetry/differential thermal analysis (TG/DTA), and advanced scanning probe microscopy (SPM). In the pure PVA films, SPM allowed us to observe ribbon-shaped domains with a different frictional and elastic contrast, which apparently originated from a correlated growth or assembly of PVA crystalline nuclei located within individual PVA clusters. The incorporation of 1.0 wt.% glycerol led to modification in shape of those domains from ribbon-like in pure PVA to rounded in PVA/glycerol 1.0 wt.% films; changes in the relative intensities of the XRD peaks and a decrease of the amorphous halo in the XRD pattern were also detected, while the DTA peak corresponding to the melting point remained at almost the same temperature. For higher glycerol content, FT-IR revealed additional glycerol-characteristic peaks presumably related to the formation of glycerol aggregates, and XRD, FT-IR, and DTA all indicated a reduction in crystallinity. For more than 2.0 wt.% glycerol, the plasticization of the films complicated the acquisition of SPM images without tip-induced surface modification. Our study contributes to the understanding of crystallinity in PVA and how it is altered by a plasticizer such as glycerol.

We investigate the shear quasinormal modes (QNMs) in the context of electron stars, focusing on the Lifshitz geometry and exploring the behavior in the small star limit. The study begins by analyzing the shear sector modes in the infrared (IR) Lifshitz limit, where the geometry approaches that of a pure Lifshitz space. We derive and solve the differential equations governing the shear modes, revealing the asymptotic behavior in terms of the Lifshitz exponent. In the special case of integer values of the exponent, we obtain analytic corrections to the solutions to extract hydrodynamical QNMs. We then explore the cases with a Lifshitz exponent equal to or greater than three, providing analytical corrections to the Lifshitz IR solutions. A special case with an exponent of two is also considered, leading to modified solutions. The study extends to finding hydrodynamical QNMs, emphasizing the role of the Lifshitz geometry. Further investigations involve the flux with real and complex frequencies, considering the off-shell Lagrangian and deriving conserved flux expressions. Our analysis focuses on the exterior of the star, modeled by the Reissner-Nordström-AdS geometry. The small star limit is examined, revealing interesting features in the behavior of fermionic excitations. In summary, we provide a detailed exploration of shear quasinormal modes in electron stars, shedding light on their behavior in Lifshitz geometry, small star limits, and the interplay between real and complex frequencies. Our findings contribute to our understanding of the dynamics of electron stars and their gravitational properties.

The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1-5 min) but low spatial resolution (2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with Deep Learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps.

Polyvinyl Alcohol (PVA)/ Graphene Quantum Dots (GQDs) polymer nanocomposite films have been prepared, and the relationship between their structural, thermal and nanoscale morphologi-cal properties, and their photoluminescent response, has been investigated. Although according to X-ray Diffraction (XRD), Fourier-Transform Infrared Spectroscopy (FT-IR) and Differential Thermal Analysis (DTA) the incorporation of GQDs does not significantly affect the percentage crystallinity of the PVA matrix, for a range of added GQD concentrations, Atomic Force Micros-copy (AFM) showed the formation of islands with apparent crystalline morphology on the surface of the PVA/GQD films, with GQD presumably acting as a nucleating agent for island growth. The incorporation of GQD also led to the formation of characteristic surface pores with increased stiffness and frictional contrast, according to Ultrasonic Force Microscopy (UFM) and Frictional Force Microscopy (FFM) data. The photoluminescence (PL) spectra of the films were found to de-pend both on the amount of GQD incorporated and on the film morphology. For GQD loads > 1,2wt%, a GQD-related band was observed at ~ 1650 cm-1 in FT-IR, along with an increase in the PL band at lower energy. For a load of ~2wt% GQD, the surface morphology was characterized by extended cluster aggregates with lower stiffness and friction than the surrounding matrix, and the PL signal decreased.

Functional Near Infrared Spectroscopy (fNIRS) systems for e-health applications usually suffer of poor signal detection mainly due to a low end-to-end signal to noise ratio of the electronics chain. Lock-In Amplifiers (LIA) historically represent a powerful technique helping to improve performances in such circumstances. In this work it has been designed and implemented a digital LIA system, based on a Zynq® Field Programmable Gate Array (FPGA), trying to explore if this technique might improve fNIRS system performances. More broadly, FPGA based solution flexibility has been investigated, with particular emphasis applied to digital filter parameters, needed in the digital LIA, and it has been evaluated its impact on the final signal detection and noise rejection capability. The realized architecture was a mixed solution between VHDL hardware modules and software ones, running within a softcore microprocessor. Experimental results have shown the goodness of the proposed solutions and comparative details among different implementation will be detailed. Finally a key aspect taken into account throughout the design was its modularity, allowing an ease increase of the input channels while avoiding the growth of the design cost of the electronics system.

Background. Escalating global plastic production, expected to reach 34,000 million tons by 2050, poses a significant threat to human and environmental well-being, particularly in aquatic ecosystems. Microplastics (MP) and nanoplastics (NP), which originate from the degradation of plastics, are of concern due to their potential bioaccumulation and uptake of pollutants. This study addresses identification methods and focuses on insect meal, a raw material for aquaculture feed. Methods. By using different techniques, the study was able to detect MP and NP in insect meal samples. Chemical digestion with KOH at 60°C efficiently removed organic matter without affecting the synthetic polymer polyethylene (PE). Filtration, confocal Raman microscopy, SEM and TEM were used for comprehensive analysis and integrity tests on PE films were performed using Raman and FTIR spectroscopy. The results showed the presence of PE microplastic particles in the insect meal, which was confirmed by correlative Raman and SEM mapping on a positively charged surface. In addition, the increased resolution of the Raman microscope identified submicrometric PE NP (800 nm). Transmission electron microscopy (TEM) with energy dispersive X-ray spectroscopy (EDX) confirmed plastic-like structures in the insect meal, highlighting the presence of PE plastics characterized by irregular shapes and some agglomeration. The higher carbon concentration in the EDX analysis supported the plastic nature, which was also confirmed by Raman spectroscopy. Conclusions. The study provides a robust method for the detection of MP and NP in insect meal and provides valuable insight into the possible presence of plastics in insect-based aquafeeds. The combination of different analytical methods increases the reliability of the results and sets the stage for future investigations that could focus on the quantification of NP and the assessment of their potential environmental impact.

Addressing global environmental problem, water splitting to produce hydrogen fuel by solar energy is getting so much attention. In the water splitting, the essential problem to solve is the development of efficient　catalysts for oxygen production. In this paper, having the prospect for a practical application of photocatalysts to artificial photosynthesis, molecular mechanisms in the current literature are briefly reviewed. At first, recent progress in the function of Mn cluster at the natural photosystem II is briefly described. The kinds of devices in which oxygen evolution reaction (OER) catalysts are used were designated; water electrolyzers, photoelectrodes, and photocatalysts. Some methods for analyzing molecular mechanism in OER catalysis, emphasized by FTIR method, are shown briefly. After describing common OER mechanisms, the molecular mechanisms are discussed for TiO2 and BiVO4 photoelectrodes with our novel data, followed by presenting of co-catalysts IrO2, RuO2, NiO2, and other metal oxides for OER catalysts. Recent reports describing OER catalysts of perovskites, layered double hydroxides (LDH), metal-organic frameworks (MOF), single atom catalysts, as well as metal complexes are reviewed. Finally, by comparing with natural photosystem, the requiring factors to improve the activity of the catalysts for artificial photosynthesis will be discussed.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

This paper explores the Wilsonian renormalization group of probe-D-branes, focusing on the Dirac-Born-Infeld (DBI) action. The analysis involves deriving the constant of motion and formulating the Wilsonian renormalization group equation. The study extends to the infrared regime, considering both zero and non-zero temperatures. Additionally, thermodynamic aspects related to the functional form of the grand potential are discussed. The investigation provides insights into the renormalization of probe-D-branes and their thermodynamic properties.