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The spectral mismatch between solar cells and incident radiation fundamentally limits their efficiencies as photons outside the optimal absorption range of the cell are lost due to transmission or thermalization. This challenge has driven research into materials and processes capable of modifying the solar spectrum to align better with the absorption characteristics of photovoltaic devices. Rare-earth-doped glasses and glass ceramics have emerged as promising candidates for addressing this limitation, exploiting energy up-conversion and down-conversion mechanisms to enhance solar cell performance. This review provides a systematic comparison of rare-earth-doped glasses, nanoparticle-embedded glass ceramics and transparent glass ceramics as spectral modification materials for photovoltaic applications, with emphasis on their underlying energy conversion mechanism. Alternative emerging approaches such as perovskite quantum dot-embedded glasses and organic luminophores are reviewed to provide a broader perspective on spectral conversion technologies. Device-level challenges and future opportunities are also discussed. It is observed that rare-earth-doped materials offer a transformative approach to overcoming the spectral mismatch problem, paving the way for more efficient and sustainable solar energy technologies.

This paper presents novel compact and high performance microstrip patch antenna by using artificial dielectric. Cylindrical rods artificial dielectric is embedded in substrate of reference patch antenna, this makes the antenna compact, reflection coefficient improves and enhancement in impedance bandwidth is achieved. Technique of designing the cylindrical rods artificial dielectric in given frequency range is also presented in this paper. Practical swarm optimization technique (PSO) is used to design and optimize the proposed antenna. Finally, RMPA with cylindrical rods embedded substrate is proposed that provides 47.2% compactness, improved return loss and bandwidth. Mathematical analysis of proposed antenna is also done to verify the simulated results of proposed antenna. The effect of rods parameters (height, radius, and distance between rods) on the performance of patch antenna is also analysed.

This paper presents the design and analysis of a broad-band patch antenna using split ring metamaterial. The SRR metamaterial structures are embedded in a unique and novel way in the patch antenna, so that subwavelength modes get introduced in the patch cavity and a broad bandwidth antenna with good performance characteristics is obtained. A rectangular microstrip patch antenna is taken as a reference antenna, which resonates at a frequency of 5.2 GHz and has an impedance bandwidth of 70 MHz. To improve the bandwidth of the patch antenna, firstly the split ring resonator (SRR) is designed according to the reference patch antenna. The optimized SRR metamaterial is placed in between the patch and ground plane of the proposed antenna. The – 10 dB impedance bandwidth of the metamaterial-embedded proposed antenna is 1.63–4.88 GHz and has an average gain of 4.5 dB. The Prototype of the proposed antenna and reference antenna is fabricated and experimental results are obtained. Experimental and simulated results are in good agreement. The presented antenna can be used for LTE, GSM, WiMAX, Bluetooth, and other wireless applications.

This paper presents a low-cost circular patch antenna sensor developed for measurement of moisture content in lentils and rice. The proposed sensor was modeled and simulated in HFSS after that the prototype was fabricated by chemically etching the FR4 material of 1.6 mm thickness. Measurements were conducted using a Vector Network Analyzer. The presented antenna sensor is resonating at 2.45 GHz frequency. Calibration equations were obtained by utilizing moisture content and reflection coefficients values. The moisture content varied between 2.04 and 11.11% based on wet weight. A good agreement between the actual and calculated values of moisture content validates that this proposed antenna sensor could serve as a moisture sensor for rice and lentil grains. For rice, the sensitivity is found to be 0.0071, and the regression coefficient is equal to 0.9976. In case of lentil, the values of the regression coefficient and sensitivity are 0.9981 and 0.0088, respectively. Furthermore, a real-time moisture sensing setup has made using a laptop with LabVIEW software, ESP32 microcontroller, VNA, and an Ethernet connection. The moisture content values can be seamlessly displayed in real time on mobile devices or laptop for convenient monitoring and analysis.

As the global population expands and protein demand also rises, the environmental and ethical issues around traditional animal-based proteins become more important. Conventional proteins are associated with several issues, such as greenhouse gas emissions, deforestation, and extensive use of water and land. However, alternative proteins (APs) originating from plants, microbes, insects, and cultured cells have the potential to overcome such problems. Such proteins not only provide a solution to the growing population but also a sustainable alternative to conventional protein sources. This review focuses on the various sources of alternative proteins, such as plant-based (oil seeds, soybeans, peas), insects (mealworms and crickets), microbial (algae and fungi), and lab-grown proteins. It investigates the extraction and production processes, such as wet and dry fractionation, enzymatic hydrolysis, fermentation, and cell culture, with a focus on efficiency, scalability, and sustainability. Furthermore, the review discusses current consumption trends and commercial acceptance of alternative proteins, taking into account taste, texture, price, and cultural preferences. Despite their potential, alternative proteins are limited by high production costs, regulatory hurdles, and market acceptance issues. The analysis continues by exploring future potential for boosting protein quality, advancing processing technology, and broadening uses in the food and industrial sectors. Overall, AP may play an important role in developing a sustainable food system, providing nutritional safety, and mitigating the environmental effects of conventional protein production.

A series of Sm 3+ and Gd 3+ -co-doped lead borate glasses having composition 65B 2 O 3 –(35–2 x )PbO– x Sm 2 O 3 – x Gd 2 O 3 (where x  = 0.5, 1.0, 1.5, 2.0 and 2.5 mol%) was prepared through melt quenching technique. Physical, structural and optical properties were investigated and analyzed. Amorphous nature of the prepared samples was confirmed by X-ray diffraction (XRD) spectra. Fourier Transform Infrared (FTIR) spectra revealed incorporation of Pb in borate glass network which signifies structural changes. UV–Visible absorption spectra consisted of several absorption peaks corresponding to the 4 f –4 f transitions of Sm 3+ luminescent center from 6 H 5/2 ground state to various excited energy levels. Optical bandgap of the prepared glass samples evaluated using Tauc’s method had also been supported by newly developed derivation of absorption spectrum fitting (DASF) method. Photoluminescence emission spectra of the prepared glass samples exhibited wide emission in visible band of Sm 3+ ions and characteristic prominent peak ( 4 G 5/2 – 6 H 7/2 ) at about 597 nm under ultraviolet excitation. Energy transfer mechanism from Gd 3+ to Sm 3+ and the process of energy down conversion witnessed in prepared glass samples indicated the potential use of rare earth-co-doped lead borate glass in high-energy radiation sensing and laser applications.

Cancer is a condition that has plagued humanity for thousands of years, with the first depictions dating back to ancient Egyptian times. However, not until recent decades have biological therapeutics been developed and refined enough to safely and effectively combat cancer. Three unique immunotherapies have gained traction in recent decades: adoptive T cell transfer, checkpoint inhibitors, and bivalent antibodies. Each has led to clinically approved therapies, as well as to therapies in preclinical and ongoing clinical trials. In this review, we outline the method by which these 3 immunotherapies function as well as any major immunotherapeutic drugs developed for treating a variety of cancers.

In the present work, twelve inorganic thermoluminescence dosimeteric (TLD) materials doped with some rare earth elements (LiF:Sm, LiBaP2O7:Eu, CaCO3:Eu, CaSO4:Dy, SrSO4:Sm, CdSO4:Dy, BaSO4:Eu, Li2B4O7:Dy, MgB4O7:Gd, Al2O3:Gd, MgAl2O4:Ce and LiCaAlF6:Eu) and three organic TLD materials (C3H7NO2, C7H8O2 and C4H6BaO4) were selected for comparative analysis on the basis of different photon sensing parameters. About nine photon sensing parameters viz. mass attenuation coefficient (MAC), linear attenuation coefficient (LAC), mean free path (mfp), half value layer (HVL), tenth value layer (TVL), effective atomic number (EAN), effective electron number (EEN), exposure buildup factor (EBF) and energy absorption buildup factor (EABF) were obtained for the selected fifteen TLDs. The simultaneous variation of these photon sensing parameters for the selected TLDs with photon energy and composition has been analyzed. The results of present comparative analysis help radiation physicists to easily select a particular dosimeter for their research laboratory from different existing compositions. All photon sensing parameters viz. MAC, LAC, mfp, HVL, TVL, EAN, EEN, EBF and EABF for selected TLDs strongly depend upon incident energy and chemical composition in lower and higher energy regions. Among the selected TLDs; BaSO4: Eu3+ offers best results (maximum values for MAC, EAN, EEN; and minimum values for mfp, HVL, TVL, EBF, EABF); whereas MgB4O7:Gd3+ offers EAN value close to tissue and less variation in most of the sensing parameters with respect to photon energy.

In preclinical models, optimum tumor control by radiation therapy incorporates CD8 T cell control of residual cancer cells. Tumor control by these CD8 T cells is negatively regulated by myeloid and T regulatory cell expansions in the tumor following radiation treatment. We demonstrate using ex vivo 3D tumor models and novel in vivo cell tracking models that radiation results in upregulation of the costimulatory molecules CD80 and CD86 on tumor macrophages and on monocytes that enter the tumor following radiation. This regulation of CD80 is not dependent on adjuvant signaling via MyD88 or STING in the myeloid cells. While we would anticipate that CD80 and CD86 would have a positive impact on anti-tumor immunity, we demonstrate that blockade of CD80 and CD86 signaling limits CD4 infiltrates in tumors and prevents the Treg expansion caused by radiation treatment. Importantly, this treatment results in improved tumor control following radiation therapy. These data link the inflammatory activation of myeloid cells in irradiated tumors to the Treg expansion following radiation and ensuing suppression of anti-tumor immunity.

QSAR analysis is one of the prominent areas in environmental toxicology, computational drug discovery, and chemical risk assessment. QSAR has transformed drug discovery by demonstrating mathematical relationships between chemical structures and pharmacological action via quantitative analysis. QSAR analysis employs a systematic workflow to ensure accurate predictions and useful insights into chemical component activity. A variety of QSAR software has been developed to aid QSAR modeling. QSAR software uses statistical and machine learning methods to predict the activity of various chemicals. They create predictive models using available data about the molecular structure and biological activity of various compounds, thus allowing researchers to better understand the qualities required for a certain biological activity. As a result, it facilitates drug discovery by allowing researchers to swiftly identify interesting chemicals for future laboratory testing. The classification is based on criteria like accessibility, type of algorithm used for model generation, or functionality. Advanced machine learning algorithms outperformed conventional linear methods such as Multiple Linear Regression (MLR).This review provides a comprehensive overview of the most widely used QSAR software, including both open source as well as commercial tools to assist researchers in choosing the best tools for their particular scientific and regulatory requirements.