Conventional Process Research Articles

TEMPERATURE VARIATION IN AN A-B BILAYER SYSTEM DURING RADIATION-INDUCED AB COMPOUND LAYER FORMATION

Metal silicide formation plays a crucial role in microelectronics, particularly in contact and interconnect technologies. Traditional silicide fabrication methods rely on high-temperature annealing, which can lead to undesirable effects such as increased surface roughness and poor electrical contact. An alternative approach is irradiation-assisted silicide formation, which offers advantages such as lower processing temperatures and improved material properties. Understanding the thermal dynamics during irradiation-induced silicide growth is essential for optimizing this process. In this study, we present a mathematical model that describes the temperature dynamics in an A (metal) - B (silicon) bilayer system under the influence of radiation-induced heating during the formation of an AB (metal silicide) compound layer. The model describes heat generation rates within three irradiated A, AB, and B layers, each with distinct material properties. In this work, we used the nickel-silicon bilayer system as a case study. The result from this study shows that the heat generation rate exhibits both linear and parabolic dependencies on layer thickness and temperature change within the nickel silicide layer during radiation-induced heating. Furthermore, the model reveals a significant finding: the temperature obtained in this study for nickel silicide growth under irradiation is lower than its formation temperature under non-irradiation conditions (e.g., conventional heating processes). This result highlights one of the key advantages of employing irradiation techniques over non-irradiation methods. Lastly, the results also show that the thermal vacancy mechanism is not the dominant atomic transport mechanism during the irradiation of the nickel-silicon bilayer system.

A Modified Deep Neural Network-based Denoiser for Pre-Processing of Diabetic Retinopathy Images

Diabetic retinopathy (DR) is a significant reason for visual impairment all over the planet. Recognizing symptoms in the fundus picture is commonly utilized in illnesses connected with the eyes like diabetic retinopathy. It requires early detection through high-quality retinal imaging. However, noise and poor contrast degrade image clarity, affecting diagnosis. To overcome these limitations, pre-processing stage plays a vital role in clinical picture handling to build the nature of fundus images. Preprocessing technique is principally used to eliminate undesirable noises and improve some image features. Image processing makes use of a variety of pre-processing methods. This study presents an exhaustive preprocessing CAD model for diabetic retinal images. By harnessing both advanced deep learning methods and conventional image processing methods, this mechanized conclusion model (computer aided design) looks to work with better analysis and the executives of diabetic retinopathy - one of the main sources of vision misfortune all around the world. We propose a novel preprocessing pipeline integrating Wiener filtering, modified CLAHE, and a deep learning-based Retinal_Denoiser to enhance DR image quality. The proposed Retinal_Denoiser is utilized during the preprocessing phase of the computer-aided design model to eliminate noise and improve quality in retinal images, utilizing deep learning-based denoising autoencoder and protecting the fundamental features of DR pictures. Preprocessing steps were utilized to increase signal-to-noise ratio, our proposed model sets another best-in-class result with a peak signal-to noise ratio (PSNR) value of 62. The high PSNR achieved by our proposed method indicates that it is more effective in preserving the image details while effectively suppressing noise. The fundamental goal of this research is to improve the image by reducing noise, improving contrast, and preserving important structures such as blood vessels and the optical disc. Our method achieves a PSNR of 62.12, surpassing conventional CNN-based, RNN-based, DnCNN-based, and other well-known denoisers.

Thermal Frontal Polymerization in Polymer Composites: Numerical Simulation and the Role of Fe3O4 Nanoparticle Fillers

Abstract Recently Thermal Frontal Polymerization (TFP) has emerged as a low-energy alternative, that enables rapid and energy-efficient manufacturing of composites. Thus, compared to conventional processes, this innovative curing and polymerization process exhibits improved efficiency and reduced environmental impact and provides a promising strategy to address sustainability challenges. However, successful TFP requires a delicate balance of reaction rates, exothermicity, and efficient heat transport into unpolymerized media while minimizing heat losses to the surroundings. In this context, sustaining TFP of polymers reinforced with highly conductive fillers is challenging due to the increased energy dissipation and reduced availability of exothermic energy as the filler content increases at the cost of resin volume. In this work, a numerical study of the TFP based manufacturing of Bisphenol A Diglycidyl Ether (BADGE) filled with Fe3O4 nanoparticles is presented. The simulation provides insight into the thermo-chemical process and into the impact of different particle filling degrees on the key characteristics of TFP, i.e., maximum attainable degree of cure, maximum temperature, front shape, and front speed.

Formation and Chemical Structure of Carbon-13 Tracer Lignin-Carbohydrate Complexes (LCCs) During Kraft Pulping

In this study, a modified synthetic method for labeling a lignin dimer (guaiacylglycerol-β-guaiacyl ether-[α-13C]) was developed. The chemical structure of the target compound was analyzed using 1H-NMR, 13C-NMR, and other analytical techniques. Then, the 13C-labeled phenolic lignin model compound was subjected to kraft pulping in the presence of xylose. Finally, the resulting reaction products were fractionated using acid precipitation and ethyl acetate extraction, and each fraction was analyzed by carbon-13 nuclear magnetic resonance (13C-NMR) and two-dimensional heteronuclear multiple quantum coherence (HMQC) spectroscopy. This aimed to investigate the occurrence of lignin–carbohydrate complexes (LCCs) during the conventional kraft pulping process. Employing ethanol as the reaction medium facilitated the bromination of 4-acetylguaiacol-[α-13C], resulting in a homogeneous reaction and significantly improving the yield of the brominated product to over 90%. Additionally, kraft pulping of the phenolic lignin model compound in the presence of xylose led to the occurrence of minor quantities of benzyl ether-type lignin–carbohydrate complex (LCC) structures, which were predominantly detected in the ethyl acetate extractive.

Sustainable Recovery of Lead from Secondary Waste in Chloride Medium: A Review

Environmental regulations on lead recycling are becoming increasingly stringent, prompting the search for sustainable alternatives to conventional high-temperature processes. Hydrometallurgical methods in chloride media have emerged as a viable option for recovering lead from mining and urban wastes, including lead anode corrosion residues, zinc leaching residues, and spent lead–acid batteries. This study reviews the key conditions for lead recovery in chloride media, highlighting the variables that optimize lead dissolution, and the potential challenges associated with these processes. The findings indicate that efficient lead recovery requires high chloride concentrations, with acidity playing a critical role depending on the relative concentrations of lead and sulfate in the solution. When lead and sulfate concentrations are similar, stable lead–chloride complexes form within a pH range of 0 to 6.0. However, at higher sulfate concentrations, the pH range narrows significantly to 0 to 2.0, necessitating a more acidic environment for effective dissolution. Chloride media offer a distinct advantage through the formation of stable lead–chloride complexes, whose stability is influenced by chloride concentration, sulfate concentration, pH, and redox potential. Moreover, this approach provides a sustainable alternative that could integrate seawater into industrial processes, particularly in regions facing water scarcity.

Security Enhancement in 5G Networks by Identifying Attacks Using Optimized Cosine Convolutional Neural Network

ABSTRACTThe exponential growth of 5G networks has introduced advanced capabilities but also heightened susceptibility to sophisticated cyberattacks. To address this, a robust and optimized security framework is proposed, leveraging a Cosine Convolutional Neural Network (CCNN) for attack detection. By emphasizing angular correlations in data, the CCNN improves feature extraction by substituting cosine similarity‐based adjustments for conventional convolution processes. To maximize the CCNN's performance, the Exponential Distribution Optimizer (EDO) is employed optimize CCNN. The optimal configuration of CCNN is achieved using EDO's probabilistic search mechanism, which is inspired by exponential distribution and helps to maintain a balanced exploration‐exploitation strategy. This integrated approach significantly improves detection accuracy, robustness, and scalability while maintaining low computational overhead. Comprehensive evaluations demonstrate the model's efficacy in identifying diverse attack patterns in 5G networks, outperforming conventional methods. The proposed framework establishes a new benchmark for secure, intelligent 5G infrastructures, contributing to the advancement of cybersecurity in next‐generation networks. The introduced approach attains higher accuracy of 99%.

New Strategies for Sustainable Biofuel Production: Pyrolytic Poly-Generation of Biomass

Biomass serves as a promising renewable and sustainable feedstock for energy production through thermochemical conversion. It can be transformed into sustainable biofuels by means of pyrolysis. Among these methods, the pyrolytic poly-generation of biomass, a novel biomass thermal conversion technology, can concurrently produce three valuable products, namely biochar, bio-oil, and combustible gas, without generating any byproducts. In contrast, conventional thermal conversion processes, such as carbonization for biochar, liquefaction for bio-oil, gasification for syngas, and combustion for heat, only yield single products, have limited efficiency, and give rise to byproducts. Clearly, pyrolytic poly-generation holds significant advantages over conventional thermal conversion processes. Nevertheless, the pyrolytic poly-generation process and its products are remarkably influenced by numerous factors, including the raw biomass properties, pretreatment methods, operating parameters, and catalysts. This article reviews the processing parameters and technology for biomass pyrolytic poly-generation, and also explores future research areas, with the aim of identifying research gaps and promoting its industrial implementation.

Visible Light-Driven Acetaldehyde Production from CO2 and H2O via Synergistic Vacancies and Atomically Dispersed Cu Sites.

Acetaldehyde (CH3CHO) is of great industrial importance and serves as a key intermediate in various organic transformations. Photocatalytic production of acetaldehyde from CO2 represents a sustainable route compared to conventional oxidation processes. However, current photocatalytic systems often face challenges, including limited product selectivity and dependence on sacrificial reagents. Here, we present a Cd0.6Zn0.4S (CZS) photocatalyst co-modified with sulfur vacancies and atomically dispersed Cu (Cu/CZS-Vs) for the efficient conversion of CO2 to acetaldehyde. Charge density analysis reveals that sulfur vacancies induce charge accumulation around the adjacent metal atoms, creating active sites that strongly anchor CO2 and H+, thereby promoting CO2 conversion while suppressing the competing hydrogen evolution reaction. The atomically dispersed Cu sites facilitate the conversion of key intermediates (i.e., *CHO and *CO) to the crucial C2 intermediate *OCCHO, which can subsequently be converted to acetaldehyde. As a result, this catalyst achieves an acetaldehyde yield of 121.5 μmol g-1 h-1 with a selectivity of 80% via photocatalytic CO2 conversion in the absence of sacrificial agents, along with a quantum efficiency of ca. 0.53% at 400 nm, underscoring its potential for practical CO2 conversion applications. These results are expected to pave the way for future developments in green chemical processes.

Degradation of diclofenac using advanced oxidation processes: a review

Diclofenac (DCF) is one of the emerging compounds in the environment. There are many sources of diclofenac, such as effluent of pharmaceutical industries, wastewater treatment plant effluent, and domestic wastewater. It requires advanced treatment because it cannot be removed from water and sludges using the conventional wastewater treatment process. Catalytic and free radical methods also known as advanced oxidation process (AOP) can degrade large and complex organic compounds into smaller ones. In this review, each AOP method is critically assessed for the removal of DCF in water.

From Batch to Streaming: Building Real-time Inference Pipelines for Machine Learning

Modern machine learning applications are experiencing a fundamental shift from traditional batch processing toward real-time inference pipelines, driven by the increasing demand for timely and context-aware predictions. This article comprehensively explores different training and serving architectures, ranging from conventional batch processing to sophisticated streaming approaches. It examines the evolution of ML pipelines, discussing the advantages and challenges of various architectural patterns, including batch training with batch predictions, batch training with streaming predictions, and fully streaming approaches. The article delves into the implementation considerations for each architecture, addressing critical challenges such as data freshness, concept drift, and model degradation. It also explores continual learning systems, representing the cutting edge of adaptive ML architectures. The article includes a detailed analysis of best practices for implementation, covering architecture selection, system design considerations, and operational excellence. Through this systematic examination, the article provides practitioners with a structured framework for selecting and implementing appropriate ML pipeline architectures based on their specific requirements and constraints.

Efficient Generation of Negative Hydrogen with Bimetallic-Ternary-Structured Catalysts for Nitrobenzene Hydrogenation.

Transition metal-catalyzed transfer hydrogenation (TH) with in situ negative hydrogen (H-) has received extensive attention as an alternative to conventional high-pressure hydrogenation processes. However, the insufficient activity of hydrogen production and unclear the conversion process of hydrogenation remain a great challenge. In this work, brand new bimetallic ternary-structured catalysts (Ru1+nM1-TiO2, M=Co, Cu, Fe, Ni) were synthesized to efficiently generate H- donors from ammonia borane (AB, NH3BH3) for nitrobenzene hydrogenation under moderate conditions. The Ru1+nCo1-TiO2 catalysts exhibited highest activity for hydrogen production form AB hydrolysis with a TOF value of 2716 min-1. The Ru1+nCo1-TiO2 achieved >90% yields within 3-4 hours in converting nitrobenzene to anilines using AB. Mechanistic studies revealed that the high hydrolysis activity was due to that the Ru SA and Co SA sites of the bimetallic-ternary-structured catalyst required the lowest energy for the activation of AB and H2O, respectively. Remarkably, the Co SA and Ru clusters exhibited an obvious synergistic effect in the TH process, which promoted the tandem hydrogenation of nitroaromatics. This work demonstrated an efficient approach to generate H- donor with bimetallic-ternary-structured catalysts in TH process and further provided new inspiration on the development of multifunctional catalysts.

Removal of Emerging Contaminants (Endocrine Disruptors) Using a Photocatalyst and Detection by High-Performance Liquid Chromatography (HPLC)

Among several types of emerging contaminants, the endocrine disruptors 17β-estradiol (E2) and 17α-ethinylestradiol (EE2) are particularly notable. These compounds are discharged into sewage systems and subsequently into water bodies, as conventional wastewater treatment processes are unable to effectively eliminate such pollutants. Therefore, the present study aimed to evaluate the possibility of removing the endocrine disruptors 17β-estradiol (E2) and 17α-ethinylestradiol (EE2) from water using the photocatalytic activity of the compound Ag3AsO4. Silver arsenate was synthesized and characterized, the quantification of the hormones E2 and EE2 was achieved by high-performance liquid chromatography with a fluorescence detector, and a validation process and some preliminary tests were performed on the photodegradation of the hormones using the Ag3AsO4 catalyst. Validation was performed, and satisfactory results were achieved: r = 0.9987 (E2), r = 0.9984 (EE2), a detection limit of 5.01 (E2) and 0.51 (EE2), a quantification limit of 15.19 (E2) and 1.54 (EE2), coefficients of variation for precision intraday and interday lower than 10.9725% and 11.3393%, respectively, and a recovery of 100.15% (E2) and 100.31% (EE2). In photodegradation studies, Ag3AsO4 showed different behavior in the presence of light for each hormone. In solution with E2, it reached a removal rate of 35% of the hormone under LED light, acting as a photocatalyst, while with EE2, it reached a removal rate of 96%; both results were obtained after 30 min of exposure to visible light. When this study is compared with other processes and materials, the high efficiency of the Ag3AsO4 photocatalyst in removing E2 and EE2, persistent emerging contaminants, becomes evident. This advancement has significant implications for wastewater treatment, offering a promising solution that can mitigate environmental impacts caused by endocrine disruptors.

Optical imaging combined with artificial intelligence in plant disease detection: a comprehensive review

Plant diseases have long been a significant agricultural concern, affecting yield and quality. Accurate and fast plant disease identification is significant to enhance its sustainable agricultural productivity. Conventional methods of disease detection are often labor-intensive. Automatic plant disease identification is initially solved through conventional image processing and machine learning approaches, which result in less accuracy. A deep learning technique is introduced to enhance the precision of predictions. This review explores the synergistic integration of hyperspectral imaging and artificial intelligence (AI) in plant disease detection. We focus on computational methods, data fusion techniques, and real-world applications, providing a comprehensive overview of current advancements and future directions.

Abstract B001: Effective generation of potent tumor-infiltrating lymphocytes (TIL) expressing regulatable membrane-bound IL15 (mbIL15) and LIGHT (TNFSF14) from immune-excluded chondrosarcoma

Abstract Background: TIL cell therapy can induce durable disease regression in solid tumors. TIL engineered with regulatable mbIL15 (cytoTIL15TM cells) demonstrate interleukin 2-independent expansion and function (Burga SITC 2023) and can lead to durable clinical responses (Amaria ASCO 2024). Furthermore, TIL engineered with co-regulated expression of mbIL15 and LIGHT show enhanced efficacy in fibroblast-containing, immunologically cold tumors (Koscso AACR 2024). Chondrosarcomas are often of mesenchymal stem-cell origin and show similar transcriptional signature as fibrotic tumors, including high expression of LTBR and HVEM (TNFRSF14; LIGHT co-receptor). Herein, we sought to determine if tumor-reactive TIL engineered with mbIL15 and LIGHT could be produced from immune-excluded tumor types, such as chondrosarcoma. Methods: This retrospective study used tumors from patients with chondrosarcoma treated at Northwestern University to characterize the tumor microenvironment using immunohistochemistry, TCR sequencing, and spatial transcriptomics. Punches from intratumoral and peritumoral areas of selected tumor sections were compared. We also prospectively collected chondrosarcoma tumors to expand TIL using a conventional (non-engineered) process or the Obsidian process. TIL were characterized via flow cytometry, TCR sequencing, and single-cell sequencing. TIL function was evaluated with LIGHT functional assays as well as co-cultures with autologous tumor spheroids, with cytotoxicity marked by caspase 3/7 staining. Results: Chondrosarcoma tumors had significantly higher peritumoral levels of cells expressing nearly every immunologic marker tested, including CD45, CD68, CD4, CD8, CD20, CD56, CD14, CD206, and MECA-79. Gene expression analysis showed that while large sections of chondrosarcomas have a more inflammatory gene expression profile compared with other similar sarcomas, these genes are confined to the tumor's periphery. TCR sequences detected in the intratumoral area appeared to be from semi-invariant alpha-beta TCR-expressing, innate-like MAIT cells, whereas a more polyclonal T-cell fraction was present in the peritumoral area (2–959 unique complementarity-determining region 3 [uCDR3] in peritumoral region, vs 460–18989 uCDR3 in peritumoral region immune clusters, n=5). Additionally, chondrosarcoma showed 2.58-fold higher expression of HVEM compared with other sarcoma types, providing a rationale for LIGHT-engineered TIL. Co-culture experiments with TIL engineered with co-regulated mbIL15 and LIGHT manufactured from chondrosarcomas demonstrated enhanced cytotoxicity against autologous spheroids compared with non-engineered TIL (ANOVA, p<0.005). Conclusions: While chondrosarcoma is usually classified as an immunologically cold tumor, we observe peritumoral immune cells in chondrosarcoma, and using the Obsidian TIL expansion process coupled with mbIL15 and LIGHT engineering generates potent tumor-specific lymphocytes. These findings support further efforts to develop TIL cell therapy for immunologically cold tumors such as chondrosarcoma. Citation Format: Zheng Ao, Rusul Al-Marayaty, Bulent Arman Aksoy, Carmela Passaro, Farres Obeidin, Rachel Burga, Nishita Roy, Samer Attar, Terrance Peabody, Juliana Ng, Weiqing Jing, Himaly Shinglot, Ali Zhang, Alonso Villasmil Ocando, Andres Alvarado, Dexue Sun, Dhruv Sethi, Jan ter Meulen, Michelle Ols, Seth M Pollack. Effective generation of potent tumor-infiltrating lymphocytes (TIL) expressing regulatable membrane-bound IL15 (mbIL15) and LIGHT (TNFSF14) from immune-excluded chondrosarcoma [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr B001.

Bioremediation of Endocrine Disruptors (EDs): A Systematic Review of Fungal Application in ED Removal from Wastewater

Endocrine disruptors (EDs), including natural estrogens, such as 17β-estradiol (E2) and synthetic chemicals (e.g., bisphenol A (BPA) and per- and polyfluoroalkyl substances (PFAS)), pose environmental and human health risks due to their ability to interfere with hormonal systems, even at trace concentrations and can lead to developmental, reproductive, and carcinogenic effects. These persistent compounds often escape removal in conventional wastewater treatment processes, leading to environmental contamination and human exposure. Given their widespread presence in wastewater and resistance to conventional treatments, the use of fungi offers a promising bioremediation strategy. This review explores the potential of fungal biodegradation, particularly using the white-rot fungus Trametes versicolor, in mitigating the estrogenic activity of EDs in wastewater. Laccase, an oxidative enzyme produced by white-rot fungus, shows high efficiency in degrading EDs, positioning fungal treatment as an eco-friendly alternative to conventional technologies. This systematic literature review was conducted using the Methodi Ordinatio, a multi-criteria decision-making methodology that allows for a structured selection of relevant studies and underscores the significant potential of fungal-based systems in addressing the global challenge of ED contamination in water environments.

Advanced Poly (Ether Ether Ketone) Separator for Lithium Metal Battery.

The development of high-performance separators is urgently needed to improve the safety and electrochemical performance of high-energy-density lithium metal batteries (LMB). Poly (ether ether ketone) (PEEK) is an ideal separator candidate due to its high chemical resistance and excellent thermal stability. However, the processing of PEEK for separators with proper porous structure is rather challenging. Beyond the conventional sulfonation process of PEEK, here, a reversible chemical modification strategy is exploited to fabricate heat-resistant PEEK separators with sophisticated hierarchical pore architecture. The lyophilic PEEK separators including dense surface layers, middle layers with horizontally aligned pore arrays, and honeycomb-structured bottom layers enable fast ion transport and uniform Li+ flux, realizing dendrite-free characteristics during the lithium deposition process. Hence, the PEEK separator assembled LiFePO4||Li battery delivers a remarkable capacity of 103.6 mAh·g-1 after 1000 cycles at 3 C, and offers more than two times longer cycle life than that of other PEEK-based separators. Even at 70 °C, a high capacity retention rate of 84.2% is achieved after 200 cycles, ensuring battery safety in high-temperature environments. Different from the commonly used surface modification strategy for functional separators, the approach reported herein exhibits a fundamental advance in separator manufacturing for future high-safety LMBs.

A Scalable, Web-Based Platform for Proteomics Data Processing, Result Storage and Analysis.

The exponential increase in proteomics data presents critical challenges for conventional processing workflows. These pipelines often consist of fragmented software packages, glued together using complex in-house scripts or error-prone manual workflows running on local hardware, which are costly to maintain and scale. The MSAID Platform offers a fully automated, managed proteomics data pipeline, consolidating formerly disjointed functions into unified, API-driven services that cover the entire process from raw data to biological insights. Backed by the cloud-native search algorithm CHIMERYS, as well as scalable cloud compute instances and data lakes, the platform facilitates efficient processing of large data sets, automation of processing via the command line, systematic result storage, analysis, and visualization. The data lake supports elastically growing storage and unified query capabilities, facilitating large-scale analyses and efficient reuse of previously processed data, such as aggregating longitudinally acquired studies. Users interact with the platform via a web interface, CLI client, or API, providing flexible, automated access. Readily available tools for accessing result data include browser-based interrogation and one-click visualizations for statistical analysis. The platform streamlines research processes, making advanced and automated proteomic workflows accessible to a broader range of scientists. The MSAID Platform is globally available via https://platform.msaid.io.

Microstructural evolution of a low-carbon steel fabricated via wire-arc additive manufacturing: the effect of interpass time and number of layers

Abstract Wire-arc additive manufacturing (WAAM) provides advantages in deposition rate and design complexity compared to conventional fabrication processes. However, despite its advantages, studies on the influence of processing parameters on the microstructural evolution of WAAM components remain limited. To address the gap, this work examines the effects of interpass time and build height on microstructural development and microhardness across different regions of WAAM single-bead structures. Specifically, mild steel samples were fabricated following a two-factor, three-level, single-replicate design of experiments. Their microstructures were analyzed using optical microscopy (OM), scanning electron microscopy (SEM), and electron backscatter diffraction (EBSD) techniques, while phase transformation histories were correlated with different printing configurations through parent austenite grain reconstruction. The findings reveal that single-layer deposits exhibit a dual-phase microstructure consisting of ferrite and martensite. Furthermore, the addition of subsequent layers increases the ferrite phase fraction in the first layer, leading to a reduction in microhardness measurements. Interpass time and build height were found to influence the local morphology of parent grains, phase distribution, and grain topology. These results provide insights into the effects of thermal cycling and deposition strategies on solidification and grain growth associated with fabrication of mild steels. A deeper understanding of these relationships could enable process optimization for modifying microstructure and mechanical properties in WAAM-fabricated components, resulting to improved performance in structural and engineering applications.

Rapid Thermal Selenization Enhanced Efficiency in Sb2Se3 Thin Film Solar Cells with Superstrate Configuration.

Antimony selenide (Sb2Se3) is a promising material for solar energy conversion due to its low toxicity, high stability, and excellent light absorption capabilities. However, Sb2Se3 films produced via physical vapor deposition often exhibit Se-deficient surfaces, which result in a high carrier recombination and poor device performance. The conventional selenization process was used to address selenium loss in Sb2Se3 solar cells with a substrate configuration. However, this traditional selenization method is not suitable for superstrated Sb2Se3 devices with the window layer buried underneath the Sb2Se3 light absorber layer, as it can lead to significant diffusion of the window layer material into Sb2Se3 and damage the device. In this work, we have demonstrated a rapid thermal selenization (RTS) technique that can effectively selenize the Sb2Se3 absorber layer while preventing the S diffusion from the buried CdS window layer into the Sb2Se3 absorber layer. The RTS technique significantly reduces carrier recombination loss and carrier transport resistance and can achieve the highest efficiency of 8.25%. Overall, the RTS method presents a promising approach for enhancing low-dimensional chalcogenide thin films for emerging superstrate chalcogenide solar cell applications.

Enhanced multi-mode squeezing via a balanced regime in dual-four-wave mixing

Abstract The generation of multi-mode squeezing using four-wave mixing process has attracted considerable attention, offering significant potential for advancements in quantum communication, quantum key distribution, and quantum-enhanced sensing. In this paper, we propose a balanced regime within the dual-four-wave mixing (DFWM) process to theoretically generate quantum-enhanced multi-mode squeezed light. By operating at a plane-balanced regime, the relative intensity squeezing noise factor of the multi-mode beams can be made to approach zero theoretically, thus achieving perfect multi-mode squeezed states. Moreover, the generated beams at the plane-balanced regime exhibit quantum-enhanced entanglement superior to that of conventional four-wave mixing processes, offering significant implications for quantum information processing. The DFWM process can also be extended to multi-spatial-mode squeezing with a spatial-balanced regime, where the attainable squeezing degree is enhanced by a factor of G N , where N denotes mode numbers and G denotes the gain, facilitating simply experimental implementation within a single atomic system. The proposed method paves the way for practical applications in high-precision measurements, robust quantum communication networks, and advanced quantum information processing systems.