Electronic, thermoelectric and optical properties of halide double perovskites: A DFT study using GGA, TB-mBJ, and HSE06.

• Optical fibre LSPR sensor optimised by comparing AuNP deposition times. • Maximum sensitivity (257 nm/RIU) and strong linearity (R 2 = 0.99) achieved with 1-hour AuNP immersion. • Sensor demonstrated excellent reproducibility, repeatability, and stability. • Findings support the rational design of low-cost fibre-tip biosensors for detection of TNF-α with LoD of 5.86 pg/mL. This research explores the development and refinement of a localised surface plasmon resonance (LSPR) biosensor using gold nanoparticles (AuNPs) for enhanced biomarker detection. The study aims to address the challenge of cost-effective fabrication while achieving high sensitivity, with a focus on optimising the AuNP deposition process on an optical fibre’s end face. The biosensor was constructed using a silane-based method, particularly aminosilanisation, allowing AuNPs to attach to the fibre’s tip surface. Different deposition times were tested, ranging from 1 to 6 h, to determine their impact on the sensor’s sensitivity to refractive index changes. The findings revealed that the 1-hour deposition time yielded the optimal balance of sensitivity (257.05 nm/RIU) and linearity (R2 = 0.9917). In contrast, longer deposition times led to reduced effectiveness due to excessive binding of AuNPs and reduced spatial separation between them. The sensor demonstrated excellent reproducibility, repeatability and stability. Beyond refractive index sensing, we further functionalised the sensor and successfully demonstrated its ability to detect tumour necrosis factor-alpha (TNF-α), a key inflammatory biomarker, with a clinically relevant limit of detection (LoD) of 5.86 pg/ml. This optimised LSPR biosensor platform demonstrates strong potential for future development towards cytokine detection in clinical settings.

Terahertz multi-band tunable refractive index sensing graphene absorber based on surface plasmon resonance

Accurate differentiation between non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) is crucial for optimising treatment strategies and improving patient outcomes in lung cancer management. Early and precise classification supports tailored therapeutic decisions and enhances prognosis prediction. This study develops a novel method to use machine learning models trained with physical science features extracted from pathological images to classify lung cancer into NSCLC and SCLC with high accuracy and robustness. Physical science features were employed to acquire quantitative cellular microarchitecture of cancer cells from histopathological images. Random Forest algorithm was applied to identify the most informative 20 features. Next, the selected top features were used to train and evaluate four machine learning classifiers: Support Vector Machine (SVM), Gradient Boosting, Logistic Regression, and Decision Tree. The dataset included pathological images from 240 histologically confirmed lung cancer cases, divided randomly into training and validation sets (80% train, 20% test). Then, model performance was evaluated using accuracy, recall, F1 score, and area under the receiver operating characteristic curve (AUC), with robustness validation via fivefold cross-validation. Logistic Regression achieved the highest overall performance, with a median accuracy near 90% and an AUC consistently above 0.90 across fivefold cross-validation. SVM and Gradient Boosting followed closely, each surpassing 0.90 in AUC, demonstrating reliable discrimination between NSCLC and SCLC. Decision Tree showed broader variability, though it maintained acceptable recall for SCLC. Random Forest feature selection revealed refractive index percentiles and polarisation histograms as top contributors to model performance. Machine learning models trained on physical science features have the potential to serve as a highly accurate and robust framework for differentiating NSCLC from SCLC.

Terahertz (THz) spectroscopy has recently gained significant attention as a powerful tool for biomacromolecule detection due to its exceptional sensitivity in capturing molecular fingerprints, attributed to its unique wavelength range. THz biosensing platforms offer distinct advantages in identifying molecular rotational and vibrational states, making them effective for high-sensitivity, high-precision biomacromolecule analysis. This review highlights recent advancements in THz biosensing platforms, focusing on their spectroscopy interpretation and practical applications. We begin by introducing commonly used THz equipment and various metamaterials, followed by an overview of the key spectroscopy interpretation strategies for THz spectral analysis, including refractive index sensing and molecular fingerprint recognition. We also summarize the machine learning (ML) methods applied to enhance spectral analysis in THz biosensing platforms. Lastly, we provide an integrated perspective on the spectroscopy interpretation, clinical applications, and recent technological advancements in THz biosensing platforms, with an emphasis on how to construct a standardized THz-based biosensing workflow. We believe THz biosensing platforms hold immense potential to revolutionize molecular detection by enabling precise, label-free, and highly sensitive analysis of biomacromolecule interactions, paving the way for breakthroughs in medical diagnostics, environmental monitoring, and biochemical research.

Modelling and fabrication advances in fiber Bragg grating sensors for refractive index–based detection of water contaminants: a technical review

High-Q mid-infrared refractive index sensor based on Fano resonance in an all-dielectric double-rod structure

Ablation time effect on gold/Arcangelisia flava (L.) Merr nanohybrids prepared by Q-switched PLAL for refractive index sensing

Enhanced photonic spin Hall effect and sensitive refractive index sensing in coupler-free surface plasmon resonance structures

Ultra-broadband dual-polarization multimode silicon waveguide crossing enabled by subwavelength-grating-induced bound states

An automated segmentation and biometry method for mouse axial OCT images based on Longitudinal Intensity Profile Analysis (LIPA) in mouse.

Strontium titanite with a perovskite structure is widely studied for its dielectric and optical properties and is also used as a functional oxide in dielectric layers and epitaxial substrates relevant to semiconductor-related processing environments. In this context, the present work provides an experiment-linked reconstruction of optical and dielectric response derived from valence EELS. It is important to mention that in a single crystal they are often used to discover where the properties originate because they have no grain boundaries or pores. Accordingly, a single-crystal platform enables the evaluation of intrinsic and local electronic response without microstructural contributions. This study investigates the optical and dielectric response of a (100) Nb-doped SrTiO 3 single crystal using valence EELS and Kramers-Kronig analysis (KKA) to reconstruct the complex dielectric function. Here, SrTiO 3 is addressed as a wide-bandgap functional oxide rather than as a conventional semiconductor material. The optical response shows a static refractive index of about n(0) = 2.37, a static dielectric constant of ε STO = 5.63, and a band-edge onset at E g = 2.25 eV (derived from the VEELS/KKA optical response). Cole-Cole analysis reveals a broad range of dielectric relaxation processes, suggesting complex polarization behavior tied to a distributed relaxation-time spectrum and heterogeneous polarization dynamics within the probed region. The spectra also show the plasmonic and the interband-transition regions. • VEELS/KKA derive optical and complex dielectric response of (100) Nb-doped SrTiO 3 . • Single-crystal (100) platform enables intrinsic/local response assessment. • Static refractive index from VEELS/KKA: n(0) = 2.37. • Complex dielectric function reconstructed by Kramers-Kronig analysis (KKA). • Cole-Cole separates Total Complex Dielectric Response and Local Dispersive Contribution.

Lipid droplets (LDs) are key organelles involved in lipid storage, energy metabolism, and stress adaptation, and their altered dynamics have been increasingly implicated in cancer, including Acute Lymphoblastic Leukemia (ALL). In this study, we employ Holographic Tomography in Flow Cytometry (HTFC) to perform an extensive label-free, high-throughput, and three dimensional (3D) characterization of LDs in ALL lymphocytes. We measure thousands of lymphocytes belonging to three B-ALL and three T-ALL cell lines. By avoiding any fluorescent marker, we segment LDs based on the sole refractive index (RI) contrast. Then, we perform a statistically significant analysis of both whole cells and intracellular LDs, by measuring morphological and biophysical parameters derived from the 3D RI distributions. Our approach provides for the first time a comprehensive label-free 3D mapping of LDs inside different cell lines of ALL lymphocytes. The resulting statistical characterization represents a first step toward organelle-level phenotyping in leukemia and points to the potential of HTFC for future non-invasive metabolic profiling in hematologic malignancies.

Two-dimensional (2D) Ruddlesden-Popper metal halide perovskites exhibit an unusually rich optical response, characterized by multiple sidebands, broad quasi-plateaus, and pronounced thickness-dependent spectral features. In this Review, we reassess their optical properties by examining the interplay between electronic structure, exciton fine structure, exciton-phonon coupling, and photonic effects. We show that the exceptionally large excitonic oscillator strength and high refractive index naturally give rise to excitonic stop bands and interference effects that can dominate reflection, transmission, and absorption spectra, even in nominally free-standing crystals. We further discuss the complexity of the photoluminescence response, including evidence for exciton-polaron formation, self-trapping, and defect-assisted recombination. By placing electronic, vibrational, and photonic effects on equal footing, this Review provides a unified framework for interpreting optical spectra in 2D perovskites.

Dual-band high-sensitivity refractive index sensing based on gradient grating metasurface

Complex refractive indices of materials encode fundamental information on light-matter interactions and are critical for the design of advanced photonic and optoelectronic devices. In many emerging materials, refractive indices change under external stimuli such as temperature, electric fields, or strain. Tracking these changes in-operando is critical for active photonic and optoelectronic device design, but remains challenging. Conventional methods such as ellipsometry rely on labor-intensive model fitting and are often impractical for multilayer stacks or in-operando measurements. Optical reflectometry offers a simpler alternative but suffers from ambiguous extraction of refractive index from reflectance spectra and limited applicability under dynamic modulation. Here, we present ReflectoRNN, an artificial intelligence (AI)-powered reflectometry framework based on recurrent neural networks (RNN), for real-time extraction of complex refractive indices in evolving materials. ReflectoRNN extracts refractive indices from reflectance spectra under thermal, electrical, magnetic, or mechanical stimuli. It achieves a median Pearson's correlation coefficient (PCC) of 0.998 and a relative accuracy score (RAS) of 0.968 on generated datasets. Validation experiments on MoS2 and WS2 across diverse substrates, including single-layer and multilayer dielectric stacks, and distributed Bragg reflectors (DBRs), demonstrate high accuracy and physical consistency, with temperature-dependent exciton resonance energy matching Bose-Einstein predictions. ReflectoRNN enables in-operando optical characterization of materials across complex photonic structures and offers a pathway toward automated, real-time monitoring and accelerated materials discovery.

A graphene-assisted one-dimensional photonic crystal biosensor for oral cancer detection in the mid-infrared region is theoretically proposed and analyzed using the transfer matrix method. The designed structure consists of alternating high- and low-refractive-index dielectric layers surrounding an oral-tissue defect cavity that supports a highly localized defect-mode resonance inside the photonic band gap. Variations in the refractive index of healthy and cancerous oral tissues modify the optical path length of the cavity and induce measurable resonance wavelength shifts, forming the basis of the sensing mechanism. The proposed biosensor exhibits a high refractive-index sensitivity of 1629.82nm/RIU with excellent linearity (R2 = 0.9997). In addition, the structure demonstrates a narrow resonance linewidth with an average full width at half maximum of 17.01nm and a high quality factor of 470.25. The obtained figure of merit, detection accuracy, and limit of detection are 95.81 RIU- 1, 0.0588nm- 1, and 0.0104 RIU, respectively. Reflectance and transmittance analyses confirm the formation of a stable localized defect mode, while electric-field distribution maps reveal strong electromagnetic confinement inside the oral-tissue cavity, leading to enhanced light-matter interaction and improved sensing performance. Furthermore, angular-response analysis, defect-thickness optimization, and fabrication-tolerance evaluation demonstrate the robustness and stability of the proposed design under practical operating conditions. The obtained results indicate that the proposed graphene-assisted photonic crystal biosensor provides a promising platform for highly sensitive and reliable oral cancer detection in the mid-infrared spectral region.

Transient hydrate formation during disintegration of film-coated pharmaceutical tablets.

While hydrogel optical fibers hold promises for visceral peripheral optogenetics, their utility is limited by poor tissue adhesion, unstable light delivery, and micromisplacement under physiological motion, leading to off-target illumination. To address these challenges, we developed tissue-adhesive hydrogel optical fibers (TAHOFs) integrating a poly(HEMA) light-guiding core (ncore = 1.429 ± 0.004) with a bioadhesive cladding (ncladding = 1.343 ± 0.002), achieving dual functionality through refractive index contrast (Δn = 0.086) and robust tissue integration (11.5 ± 1.8 kPa). This architecture enables efficient optical confinement with low propagation loss (0.534 ± 0.092 dB/cm) while maintaining spatial targeting fidelity under 30% tensile strain. Pancreatic implantation in freely moving ChAT-ChR2 mice demonstrated precise vagal fiber activation, effectively triggering insulin secretion through mechanically and optically stable light delivery. Integrated with subcutaneous continuous glucose monitoring, TAHOFs enabled 3-day glycemic control in diabetic models, showing stable blood glucose reduction and real-time regulation. Chronic implantation demonstrated that TAHOF supports stable in vivo adhesion on the pancreas while maintaining optogenetic function for up to 14 days. The TAHOF uniting high optical efficiency, mechanical compliance, and biological integration, offering an application-specific design strategy for optogenetic neuromodulation in moving animals, particularly mechanically dynamic and anatomically complex organs.

Computational modelling of anion-substituted quantum dots reveals that controlled oxygen incorporation into CsInTiS₄ nanostructures induces profound modifications in electronic structure and optical response. Therefore, in our study, we examined 20% anion-site oxygen incorporation into CsInTiS4 (i.e., CsInTiS3.2O0.8 QDs) using methods based on density functional theory and dielectric function optical modelling. Our results regarding structural relaxation and simulated X-ray diffraction indicate that sulfur's substitution with oxygen induces lattice contraction and structural reorganization. This is accompanied by a change in the lattice symmetry from polar [Formula: see text] to centrosymmetric P2/m in the optimized geometry. CsInTiS₃.₂O₀.₈ exhibits a pronounced bandgap contraction from [Formula: see text] [Formula: see text] in CsInTiS₄ to [Formula: see text], whilst the optical carrier concentration-to-effective mass ratio increases by [Formula: see text] to [Formula: see text]. Furthermore, it reveals a Wemple-DiDomenico dispersion energy of [Formula: see text], an oscillator strength of [Formula: see text], a static refractive index of [Formula: see text], and a high-frequency dielectric constant of [Formula: see text]. Moreover, carrier dynamics characterised by an ultrafast relaxation time of [Formula: see text] point to dominant scattering pathways intrinsic to the mixed-anion framework. These findings suggest that oxygen anion substitution plays a viable role in band structure engineering and light-matter interactions in a CsInTiS₄ parent lattice. Thus, the predicted narrow band gap, enhanced light-matter interaction, and oscillator strength make CsInTiS₃.₂O₀.₈ a strong candidate for infrared photodetection, tunable plasmonics, and third-order nonlinear photonics.