Search Results (69)

CuO/TiO₂ is a highly active visible-light-driven photocatalyst. The precise structural regulation of TiO₂ and the quantum dot-scale loading strategy of CuO have long been researching hotspots and challenges. This work presents an ingenious synthetic strategy, leveraging the photoinduced superhydrophilicity and dark-induced reversible hydrophobicity of TiO₂, coupled with carbon quantum dots (CQDs) as “seeds” to induce the in situ synthesis of CuO quantum dots (CuO QDs). Specifically, CuO QDs with an average diameter of 5–10 nm were successfully anchored onto TiO₂ nanowire arrays (TNWAs) with a diameter of 10–15 nm. By adjusting the dosage of “seeds” (CQDs), the loading amount of CuO QDs can be effectively controlled. Corresponding characterizations were performed, including ultraviolet-visible-near-infrared (UV-Vis-NIR spectroscopy) for optical absorption properties, photoluminescence (PL) spectroscopy for photoluminescent behavior, electron paramagnetic resonance (EPR) spectroscopy for free radical generation capability, and bisphenol A (BPA) degradation assays for photocatalytic performance. Loading 4.78 wt% CuO QDs can effectively inhibit the recombination of electron–hole pairs in TNWAs. Simultaneously, it prolongs the lifetime of charge carriers (photoelectrons) and enhances the yields of hydroxyl radicals (•OH) and superoxide radicals (•O₂⁻). The BPA degradation efficiency of the CuO QDs/TNWA composite is 2.4 times higher than that of TNWAs. Furthermore, we found that the loading of CuO QDs significantly modulates the depletion layer width of the P–N heterojunction, and the underlying mechanism has been discussed in detail. Full article

Accurate characterization of liquid manure properties, such as dry matter (DM), total nitrogen (TN), ammonium nitrogen (NH₄-N), and total phosphorus (TP), is essential for effective nutrient management in agriculture. This study investigates the use of near-infrared spectroscopy (NIRS) within the 941–1671 nm range, combined with advanced pre-processing and machine learning techniques to accurately predict the liquid manure properties. The predictive accuracy of NIRS was assessed by comparison with nuclear magnetic resonance (NMR) spectroscopy as a benchmark method. A number of 51 liquid manure samples were analyzed in the laboratory for the reference manure properties and scanned with NIRS and NMR. The NIR data underwent spectral pre-processing, which included two- and three-band index transformations and feature selection. Partial least squares regression (PLSR) and LASSO regression were employed to develop calibration models. According to the results, using cohort-tuned models, NIRS showed fair predictive accuracy for DM (R² = 0.78, RPD = 2.15) compared to factory-calibrated NMR (R² = 0.68, RPD = 0.81). Factory-calibrated NMR outperformed for chemical properties, with R² (RPD) of 0.89 (1.74) for TN, 0.97 (5.70) for NH₄-N, and 0.95 (2.64) for TP, versus NIRS’s 0.66 (1.68), 0.84 (2.45), and 0.84 (2.51), respectively. In this study with 51 samples, two- and three-band indices significantly enhanced NIRS performance compared to raw data, with R² increases of 34%, 57%, 25%, and 33% for DM, TN, NH₄-N, and TP, respectively. Feature selection efficiently reduced NIR spectral dimensionality without compromising the prediction accuracy. This study highlights NIRS’s potential as a portable tool for on-site manure characterization, with NMR providing superior laboratory validation, offering complementary approaches for nutrient management. Full article

Thermoelectric (TE) materials represent a critical frontier in sustainable energy conversion technologies, providing direct thermal-to-electrical energy conversion with solid-state reliability. The optimizations of TE performance demand a nuanced comprehension of structure–property relationships across diverse length scales. This review summarizes established and emerging spectroscopic and microscopic techniques used to characterize inorganic and polymer TE materials, specifically poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS). For inorganic TE, ultraviolet–visible (UV–Vis) spectroscopy, energy-dispersive X-ray (EDX) spectroscopy, and X-ray photoelectron spectroscopy (XPS) are widely applied for electronic structure characterization. For phase analysis of inorganic TE materials, Raman spectroscopy (RS), electron energy loss spectroscopy (EELS), and nuclear magnetic resonance (NMR) spectroscopy are utilized. For analyzing the surface morphology and crystalline structure, chemical scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD) are commonly used. For polymer TE materials, ultraviolet−visible–near-infrared (UV−Vis−NIR) spectroscopy and ultraviolet photoelectron spectroscopy (UPS) are generally employed for determining electronic structure. For functional group analysis of polymer TE, attenuated total reflectance–Fourier-transform infrared (ATR−FTIR) spectroscopy and RS are broadly utilized. XPS is used for elemental composition analysis of polymer TE. For the surface morphology of polymer TE, atomic force microscopic (AFM) and SEM are applied. Grazing incidence wide-angle X-ray scattering (GIWAXS) and XRD are employed for analyzing the crystalline structures of polymer TE materials. These techniques elucidate electronic, structural, morphological, and chemical properties, aiding in optimizing TE properties like conductivity, thermal stability, and mechanical strength. This review also suggests future research directions, including in situ methods and machine learning-assisted multi-dimensional spectroscopy to enhance TE performance for applications in electronic devices, energy storage, and solar cells. Full article

Functional Near-Infrared Spectroscopy is (fNIRS) a non-invasive neuroimaging technique that monitors cerebral hemodynamic responses by measuring near-infrared (NIR) light absorption caused by changes in oxygenated and deoxygenated hemoglobin concentrations. While fNIRS has been widely used in cognitive and clinical neuroscience, a key challenge persists: the lack of practical tools required for calibrating source-detector separation (SDS) to maximize sensitivity at depth (SAD) for monitoring specific cortical regions of interest to neuroscience and neuroimaging studies. This study presents DrSVision version 1.0, a standalone software developed to address this limitation. Monte Carlo (MC) simulations were performed using segmented magnetic resonance imaging (MRI) data from eight cadaveric heads to realistically model light attenuation across anatomical layers. SAD of 10–20 mm with SDS of 19–39 mm was computed. The dataset was used to train a Gaussian Process Regression (GPR)-based machine learning (ML) model that recommends optimal SDS for achieving maximal sensitivity at targeted depths. The software operates independently of any third-party platforms and provides users with region-specific calibration outputs tailored for experimental goals, supporting more precise application of fNIRS. Future developments aim to incorporate subject-specific calibration using anatomical data and broaden support for diverse and personalized experimental setups. DrSVision represents a step forward in fNIRS experimentation. Full article

The Australian pork industry has been seeking a rapid and non-destructive way to predict pork chemical components and eating quality. In this study, near-infrared spectroscopy (NIRS) and nuclear magnetic resonance (NMR) were applied to fresh pork Longissimus thoracis et lumborum (LTL) and Semimembranosus (SM) with the aim to build prediction models for intramuscular fat (IMF) content, collagen content and solubility, pH, and sensory attributes, namely tenderness, juiciness, liking of flavor and overall liking as well as investigate the effects of chemical components on pork eating quality. Results showed that the NIRS output, which was a predicted IMF content calibrated for the IMF of lamb, correlated with the chemically analyzed IMF content across both muscles. In LTL, NMR parameter p_2f was weakly correlated with IMF and pH. For the LTL, NMR parameters p₂₁ and p₂₂ were related to sensory tenderness, while T₂₂ was correlated with the liking of flavor. In both muscles, the collagen content and pH were related to all sensory attributes, and IMF was related to the liking of flavor. The chemical properties of SM were weakly correlated with those of LTL. The NIRS and NMR weakly predicted the pork chemical components and sensory properties, but more studies are required to improve the accuracy. Full article

Traditional culture plays a vital role in shaping national identity and emotional belonging, making it imperative to explore innovative strategies for its digital preservation and engagement. This study investigates how interaction design in cultural digital games influences users’ emotional experiences and cultural understanding. Centering on the Chinese intangible cultural heritage puppet manipulation, we developed an interactive cultural game with three modes: gesture-based interaction via Leap Motion, keyboard control, and passive video viewing. A multimodal evaluation framework was employed, integrating subjective questionnaires with physiological indicators, including Functional Near-Infrared Spectroscopy (fNIRS), infrared thermography (IRT), and electrodermal activity (EDA), to assess users’ emotional responses, immersion, and perception of cultural content. Results demonstrated that gesture-based interaction, which aligns closely with the embodied cultural behavior of puppet manipulation, significantly enhanced users’ emotional engagement and cultural comprehension compared to the other two modes. Moreover, fNIRS data revealed broader activation in brain regions associated with emotion regulation and cognitive control during gesture interaction. These findings underscore the importance of culturally congruent interaction design in enhancing user experience and emotional resonance in digital cultural applications. This study provides empirical evidence supporting the integration of cultural context into interaction strategies, offering valuable insights for the development of emotionally immersive systems for intangible cultural heritage preservation. Full article

Chronic pain is a dynamic, brain-wide condition that eludes effective management by conventional, static treatment approaches. Transcutaneous Electrical Nerve Stimulation (TENS), traditionally perceived as a simple and generic modality, is on the verge of a significant transformation. Guided by advances in brain-state decoding and adaptive algorithms, TENS can evolve into a precision neuromodulation system tailored to individual needs. By integrating multimodal neuroimaging—including the spatial resolution of functional magnetic resonance imaging (fMRI), the temporal sensitivity of an Electroencephalogram (EEG), and the ecological validity of functional near-infrared spectroscopy (fNIRS)—with real-time machine learning, we envision a paradigm shift from fixed stimulation protocols to personalized, closed-loop modulation. This comprehensive review outlines a translational framework to reengineer TENS from an open-loop device into a responsive, intelligent therapeutic platform. We examine the underlying neurophysiological mechanisms, artificial intelligence (AI)-driven infrastructures, and ethical considerations essential for implementing this vision in clinical practice—not only for chronic pain management but also for broader neuroadaptive healthcare applications. Full article

Objectives: To synthesize evidence on structural and functional neuroplasticity in patients after anterior cruciate ligament reconstruction (ACLR) and its clinical implications. Methods: Adhering to the PRISMA guidelines for systematic reviews and meta-analyses, a literature search was conducted using PubMed, Embase, Web of Science, Scopus, and Cochrane CENTRAL (2018–2025) using specific keyword combinations, screening the results based on predetermined inclusion and exclusion criteria. Results: Among the 27 included studies were the following: (1) sensory cortex reorganization with compensatory visual dependence (5 EEG/fMRI studies); (2) reduced motor cortex efficiency evidenced by elevated AMT (TMS, 8 studies) and decreased γ-CMC (EEG, 3 studies); (3) progressive corticospinal tract degeneration (increased radial diffusivity correlating with postoperative duration); (4) enhanced sensory-visual integration correlated with functional recovery. Conclusions: This review provides a novel synthesis of evidence from transcranial magnetic stimulation (TMS), electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), diffusion tensor imaging (DTI), and functional magnetic resonance imaging (fMRI) studies. It delineates characteristic patterns of post-ACLR structural and functional neural reorganization. Targeting visual–cognitive integration and corticospinal facilitation may optimize rehabilitation. Full article

Background/Objectives: The heterogeneity of cancer disease and the frequent ineffectiveness and resistance observed with currently available treatments highlight the importance of developing new antitumor therapies. The properties of gold nanoparticles, such as their photon-energy heating, are attractive for oncology therapy; this can be effective and localized. The combination of chemotherapy and hyperthermia is promising. Our aim was to evaluate the combination therapy of photon hyperthermia with 5-fluorouracil (5-FU) both in vitro and in vivo. Methods: This study evaluated the antitumor efficacy of a combined chemo-photothermal therapy using 5-fluorouracil (5-FU) and branched gold nanoshells (BGNSs) in a colorectal cancer model. BGNSs were synthesized via a seed-mediated method and characterized by electron microscopy and UV–vis spectroscopy, revealing an average diameter of 126.3 nm and a plasmon resonance peak at 800 nm, suitable for near-infrared (NIR) photothermal applications. In vitro assays using SW620-GFP colon cancer cells demonstrated a ≥90% reduction in cell viability after 24 h of combined treatment with 5-FU and BGNS under NIR irradiation. In vivo, xenograft-bearing nude mice received weekly intratumoral administrations of the combined therapy for four weeks. The group treated with 5-FU + BGNS + NIR exhibited a final tumor volume of 0.4 mm³ on day 28, compared to 1010 mm³ in the control group, corresponding to a tumor growth inhibition (TGI) of 100.74% (p < 0.001), which indicates not only complete inhibition of tumor growth but also regression below the initial tumor volume. Thermographic imaging confirmed that localized hyperthermia reached 45 ± 0.5 °C at the tumor site. Results: These findings suggest that the combination of 5-FU and BGNS-mediated hyperthermia may offer a promising strategy for enhancing therapeutic outcomes in patients with colorectal cancer while potentially minimizing systemic toxicity. Conclusions: This study highlights the potential of integrating nanotechnology with conventional chemotherapy for more effective and targeted cancer treatment. Full article

Nowadays, the development of the food industry and economic recovery have driven escalating consumer demands for high-quality, nutritious, and safe food products, and spectroscopic technologies are increasingly prominent as essential tools for food quality inspection. Concurrently, the rapid rise of artificial intelligence (AI) has created new opportunities for food quality detection. As a critical branch of AI, deep learning synergizes with spectroscopic technologies to enhance spectral data processing accuracy, enable real-time decision making, and address challenges from complex matrices and spectral noise. This review summarizes six cutting-edge nondestructive spectroscopic and imaging technologies, near-infrared/mid-infrared spectroscopy, Raman spectroscopy, fluorescence spectroscopy, hyperspectral imaging (spanning the UV, visible, and NIR regions, to simultaneously capture both spatial distribution and spectral signatures of sample constituents), terahertz spectroscopy, and nuclear magnetic resonance (NMR), along with their transformative applications. We systematically elucidate the fundamental principles and distinctive merits of each technological approach, with a particular focus on their deep learning-based integration with spectral fusion techniques and hybrid spectral-heterogeneous fusion methodologies. Our analysis reveals that the synergy between spectroscopic technologies and deep learning demonstrates unparalleled superiority in speed, precision, and non-invasiveness. Future research should prioritize three directions: multimodal integration of spectroscopic technologies, edge computing in portable devices, and AI-driven applications, ultimately establishing a high-precision and sustainable food quality inspection system spanning from production to consumption. Full article

Albumin-based nanoparticles are promising drug delivery systems due to their biocompatibility, biodegradability, and ability to improve targeted drug release. Among various preparation methods, radiation-induced cross-linking in the presence of ethanol has been proposed in the literature as an effective method for producing protein nanoparticles with preserved bioactivity and controlled size. However, the mechanisms by which ethanol radicals contribute to protein aggregation remain insufficiently understood. In this study, we investigate the role of ethanol in the aggregation of albumins to determine whether its presence is necessary or beneficial for nanoparticle formation. Using pulse radiolysis, spectroscopy methods, resonance light scattering (RLS), and near-infrared (NIR) spectroscopy, we examined aqueous ethanol solutions of albumins before and after irradiation. Our results show that ethanol concentrations above 40% (v/v) significantly promote both radiation-induced and spontaneous protein aggregation. Mechanistic analysis indicates that ethanol radicals react with albumin similarly to hydrated electrons, mainly targeting disulfide bridges. This reaction leads to the formation of sulfur-centered radicals and the formation of intermolecular disulfide bonds that stabilize protein nanostructures by excluding the formation of dityrosine bridges, as described in the literature. In contrast, ethanol concentration below 40% does not favor the radiation-induced aggregation compared to the solution containing t-BuOH. These results provide novel insights into the role of organic cosolvents in protein aggregation and contribute to a broader understanding of the mechanisms of formation of albumin-based nanoparticles using ionizing radiation. Full article

A platform for converting near-infrared (NIR) laser power modulation into the self-mixing (SM) signal of a quantum cascade laser (QCL) operating at terahertz (THz) frequencies is introduced. This approach is based on laser feedback interferometry (LFI) with a THz QCL using a metal-coated silicon nitride trampoline membrane resonator as both the external QCL laser cavity and the mechanical coupling element of the two-laser hybrid system. We show that the membrane response can be controlled with high precision and stability both in its dynamic (i.e., piezo-electrically actuated) and static state via photothermally induced NIR laser excitation. The responsivity to nanometric external cavity variations and robustness to optical feedback of the QCL LFI apparatus allows a highly sensitive and reliable transfer of the NIR power modulation into the QCL SM voltage, with a bandwidth limited by the thermal response time of the membrane resonator. Interestingly, a dual information conversion is possible thanks to the accurate thermal tuning of the membrane resonance frequency shift and displacement. Overall, the proposed apparatus can be exploited for the precise opto-mechanical control of QCL operation with advanced applications in LFI imaging and spectroscopy and in coherent optical communication. Full article

The synthesis of two polycyclic aromatic hydrocarbon (PAH) monoradicals, terbenzoolympicenyl radical (BOR1) and tetrabenzoolympicenyl radical (BOR2), is reported. One-electron oxidation of both BOR1 and BOR2 yielded stable cationic species BOR1+ and BOR2+, whose structures were unambiguously characterized using 2D nuclear magnetic resonance (NMR) spectroscopy. The physical properties of BOR1 and BOR2 were investigated by means of electron paramagnetic resonance (EPR), UV-vis-NIR, cyclic voltammetry (CV), and density functional theory (DFT) calculations. BOR1+ and BOR2+ exhibited intense near-infrared (NIR) absorption, which may be of potential use in the biological fields. Full article

The use of biomass feedstocks for producing high-value-added chemicals is gaining significant attention in the academic community. In this study, near-infrared carbon dots (NIR-CDs) with antimicrobial and bioimaging functions were prepared from Epigynum auritum branches and leaves using a novel green synthesis approach. The spectral properties of the synthesized NIR-CDs were characterized by ultraviolet–visible (UV-Vis) absorption and fluorescence spectroscopy. The crystal structures of the NIR-CDs were further characterized by high-resolution transmission electron microscopy (HR-TEM), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR), nuclear magnetic resonance (NMR), and X-ray diffraction (XRD). The NIR-CDs exhibited minimal toxicity, excellent biocompatibility, and high penetrability in both in vivo and in vitro environments, making them ideal luminescent probes for bioimaging applications. Moreover, the antimicrobial activity of NIR-CDs was tested against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), showing significant bacterial growth inhibition. The antimicrobial effect is likely attributed to the NIR-CDs disrupting the cell membrane integrity, leading to the leakage of the intracellular contents. Therefore, NIR-CDs hold promise as fluorescent bioimaging probes and antimicrobial agents. Full article

Eggs are a rich food source of proteins, fats, vitamins, minerals, and other nutrients. However, the egg industry faces some challenges such as microbial invasion due to environmental factors, leading to damage and reduced usability. Therefore, detecting the freshness of raw eggs using various technologies, including traditional and non-destructive methods, can overcome these challenges. As the traditional methods of assessing egg freshness are often subjective and time-consuming, modern non-destructive technologies, including near-infrared (NIR) spectroscopy, Raman spectroscopy, fluorescence spectroscopy, computer vision (color imaging), hyperspectral imaging, electronic noses, and nuclear magnetic resonance, have offered objective and rapid results to address these limitations. The current review summarizes and discusses the recent advances and developments in applying non-destructive technologies for detecting raw egg freshness. Some of these technologies such as NIR spectroscopy, computer vision, and hyperspectral imaging have achieved an accuracy of more than 96% in detecting egg freshness. Therefore, this review provides an overview of the current trends in the state-of-the-art non-destructive technologies recently utilized in detecting the freshness of raw eggs. This review can contribute significantly to the field of emerging technologies in this research track and pique the interests of both food scientists and industry professionals. Full article