Synthesis Technique Research Articles

Citationchaser: A tool for citation chasing in academic searching

This review evaluates the Citationchaser tool as a research support application for systematic reviews, specifically focusing on its functionality and reliability in citation chasing. Citation chasing, a valuable technique in evidence synthesis, enables researchers to trace references backward (articles cited by a work) and forward (articles citing a work). This study highlights Citationchaser’s contribution to making these processes efficient and accessible. The review evaluates Citationchaser's performance by examining its dependency on the Lens.org database and comparing its citation-chasing capabilities with traditional bibliometric tools. Key features of the Citationchaser web application (Shiny app), including backward and forward citation chasing, are analyzed using a sample dataset to assess retrieval accuracy and citation coverage. Limitations, such as database dependency, update delays, and network visualization, are also discussed. Citationchaser streamlines citation chasing by automating the retrieval of references and citations, making it a valuable tool for researchers with limited access to traditional citation indexes. However, its reliance on Lens.org limits its coverage, and the discontinuation of Microsoft Academic Graph poses challenges to its comprehensiveness. Additionally, the ‘Network’ and ‘Analysis’ feature lacks the sophistication required for effective citation mapping, suggesting that researchers may need to complement Citationchaser with other bibliometric tools for advanced analyses. This review provides the first critical assessment of Citationchaser as a citation-chasing tool for systematic reviews. By examining its strengths, limitations, and future potential, this study offers insights into how Citationchaser could evolve to meet researchers' needs more comprehensively, positioning it as a potentially valuable addition to research support services for systematic reviews.

Titanium Mxene: A Promising Material for Next‐Generation Optical Biosensors and Machine Learning Integration

AbstractNano biosensors based on MXenes have been emerging as a promising tool in the detection of biomarkers, for the discrimination of diseases and in the detection of environmental pollutants. Their potential in sensing applications has also drawn a lot of attention to their unique qualities such as their high conductivity, huge surface area, outstanding hydrophilicity, biocompatibility, and simplicity of surface functionalization. The development of scalable synthesis techniques is essential to the large‐scale manufacturing and broad application of MXene‐based sensors. Furthermore, the stability of the MXene layers in diverse environmental circumstances continues to be a difficulty for their practical application. To increase the dependability and precision of MXene‐based sensors, their selectivity must be increased through functionalization and tuning. With innovative technologies like machine learning, MXene biosensor is now taken advantage of new opportunities. Personalized healthcare solutions, remote data analysis, and real‐time monitoring are all possible when MXene sensors and AI algorithms work together. Herein, the optical properties, synthesis approaches, role of MXene biosensors in machine learning, its significant challenges and future prospects of MXene‐based nano(bio)sensors are deliberated.

Advances in Valorization of Biomass-Derived Glycolic Acid Toward Polyglycolic Acid Production

Glycolic acid (GA) is a versatile two-carbon organic chemical with multiple applications in industry and daily life. Currently, GA production depends heavily on the coal chemical industry. In this context, the sustainable production of GA from renewable resources has garnered significant attention. With the design and development of various catalytic systems, the yield of GA from biomass-based feedstocks has been improved observably. Poly(glycolic acid) (PGA) is an aliphatic polyester that exhibits a unique crystalline structure, excellent gas barrier properties, high mechanical strength, superior biocompatibility, and biodegradability. It has a wide range of applications in various fields, such as medical devices, oil extraction, bottle materials, film materials, and textile materials. This article comprehensively elaborates on the methods for the biomass-based synthesis of glycolic acid, the precursor of polyglycolic acid (PGA), as well as the preparation process of PGA. It fills the research gap regarding the sources of biomass raw materials for polyglycolic acid. Additionally, it delves into various modification strategies for PGA and provides an overview of its current applications in multiple fields, including biomedicine, food packaging materials, oil and gas resource development, and agricultural cultivation. The purpose of this article is to provide comprehensive reference information on the synthesis techniques, modification methods, and practical applications of PGA. Furthermore, it offers guidance for research on biodegradable plastics and the biomass-based synthesis of glycolic acid.

SYNTHESIS AND RESEARCH OF THE CATALYTIC SYSTEM xAlPO4 . yCu3(PO4)2

Individual orthophosphate catalysts AlPO4 and Cu3(PO4)2 were synthesized. A new synthesis technique was developed and a new complex aluminum-copper (II) orthophosphate catalytic system of the type xAlPO4·yCu3(PO4)2 was obtained based on them (К-1–К-7), which has the predicted physical and chemical properties. The content of both orthophosphates in the catalyst structure varies in the range of 0.5 - 99.5 wt. %. At the final heat treatment temperature (700oC), all synthesized complex catalysts K-1–K-7 are X-ray crystalline. The process of thermal treatment of catalysts contributes to their gradual dehydration and release of water from the structure of the samples, as well as the occurrence of characteristic endothermic effects at the appropriate temperatures. At high temperatures of heat treatment (about 500 oC), the processes of crystallization of the structures are observed on the synthesized samples K-1–K-7. Heating of all samples of a complex catalytic orthophosphate system of the type xAlPO4.yCu3(PO4)2 at temperatures above 700 oC leads to their complete dehydration and the formation of anhydrous salts. It can be predicted that the synthesized new complex oxide catalytic heterogeneous systems of the type xAlPO4.yCu3(PO4)2 have been will show improved both acidic surface properties and catalytic parameters: activity and selectivity in reactions of partial oxidation of n-alkanes to valuable products.

Evaluation of the effect of biofeedback system driven by optoelectronic conjugate materials in VR exercise rehabilitation

Presently, Exercise Rehabilitation is crucial to restoring and improving a person’s well-being in physical fitness. The present research investigates the significance of Optoelectronic Conjugate Materials (OCM) in Biofeedback Systems to enhance the effectiveness of Virtual Reality (VR) Exercise Rehabilitation. This work presents the Design of Biofeedback System-driven Optoelectronic Conjugate Materials (DBS-OCM), which offers a novel methodology to enhance VR Exercise Rehabilitation. The DBS-OCM framework amalgamates synthesis and material characterization techniques for tissue healing during VR Exercise Rehabilitation. The present research aims to elucidate a resistance trainer’s design and preparation methodology during the Evaluation of the Effect of a Biofeedback System Driven by OCM. This approach seeks to optimize the flexibility of the system to cater to individual requirements during VR Exercise Rehabilitation. The experimental findings demonstrate notable advancements, including a 93.9% augmentation in tissue regeneration, a 95.5% enhancement in the efficacy of resistance training, and a 95.5% boost in involvement during virtual reality exercise. The results highlight the DBS-OCM’s capacity to enhance the Biofeedback System’s efficiency Driven by Optoelectronic Conjugate Materials in VR Exercise Rehabilitation. These findings provide essential insights that have the potential to shape future research endeavors and facilitate the development of practical applications in the domain of VR Exercise Rehabilitation.

A Comprehensive Review of MXene-Based Emerging Materials for Energy Storage Applications and Future perspectives.

MXenes is a rapidly emerging class of two-dimensional (2D) materials. It exhibits unique properties that make it suitable for a wide range of applications. This review provides a comprehensive overview of the synthesis and processing techniques for MXenes including both bottom-up and top-down approaches. The synthesis of MXene-based composites is explored in detail focusing on MXene-carbon composites, MXene-metal oxides, MXene-metal sulfides, MXene-polymer composites and MXene-ceramic composites. Key properties of MXenes are examined including structural, electrical, morphological, optical, mechanical, chemical stability, electrical and thermal properties, conductivity, magnetic properties, dielectric charge and catalytic properties. Characterization techniques used to study these properties are also reviewed. Their 2D structure provides a high surface area and unique interlayer spacing, making MXenes ideal for applications in energy storage devices (like supercapacitors and batteries) where surface area and ion transport are critical for performance. The diverse applications of MXenes are presented emphasizing their use in batteries, catalysis, sensors, environmental remediation and supercapacitors. Special attention is given to the supercapacitor applications of MXenes of their potential in energy storage devices. Due to their high capacitance, fast charge/discharge rates, and excellent stability, MXenes are used in supercapacitors, lithium-ion batteries, and sodium-ion batteries.

HoloGaussian Digital Twin: Reconstructing 3D Scenes with Gaussian Splatting for Tabletop Hologram Visualization of Real Environments

Several studies have explored the use of hologram technology in architecture and urban design, demonstrating its feasibility. Holograms can represent 3D spatial data and offer an immersive experience, potentially replacing traditional methods such as physical 3D and offering a promising alternative to mixed-reality display technologies. Holograms can visualize realistic scenes such as buildings, cityscapes, and landscapes using the novel view synthesis technique. This study examines the suitability of spatial data collected through the Gaussian splatting method for tabletop hologram visualization. Recent advancements in Gaussian splatting algorithms allow for real-time spatial data collection of a higher quality compared to photogrammetry and neural radiance fields. Both hologram visualization and Gaussian splatting share similarities in that they recreate 3D scenes without the need for mesh reconstruction. In this research, unmanned aerial vehicle-acquired primary image data were processed for 3D reconstruction using Gaussian splatting techniques and subsequently visualized through holographic displays. Two experimental environments were used, namely, a building and a university campus. As a result, 3D Gaussian data have proven to be an ideal spatial data source for hologram visualization, offering new possibilities for real-time motion holograms of real environments and digital twins.

Advances in CuO Nanostructure-Based Glucose Biosensors: From Enzymes to Direct Oxidation

Copper oxide (CuO) nanostructures have gained popularity in glucose biosensor development due to their excellent electrochemical properties, affordability and ease of fabrication. This review examines the progress of glucose biosensors based on CuO nanostructures and shows differences between enzyme-based and enzyme-dependent systems. Enzyme-based glucose oxidase sensors offer high specificity but are hampered by difficulties associated with enzyme stability at different temperatures, pH and interfering inhibitors. Unlike nonenzyme-based sensors, those using glucose oxidation directly offer high sensitivity and long working life but are struggling with specificity. The main drivers of these sensors are nanostructure morphology, synthesis techniques, surface modifications and electrode materials. The switch from more conventional CuO to sophisticated nanostructures significantly improved the sensitivity and durability of the sensor. This research focuses on optimizing these variables to address existing limitations and to advance CuO nanostructures as leads for next-generation glucose biosensing technologies.

Green Synthesis Techniques for Sulphur Nanoparticles: Current Methods and Future Perspectives

Abstract: In recent years, cancer has emerged as a significant public health challenge, prompting extensive research into the development of innovative anticancer therapies capable of selectively inducing cell death or halting the proliferation of cancer cells. Harnessing the distinctive characteristics of nanomaterials, advancements in nanotechnology have played a pivotal role in the progression of nanomedicine for cancer treatment. Various nanomaterials, such as gold, silver, silica, and carbon nanoparticles, have been investigated for their potential in drug delivery systems. Meanwhile, sulfur, with its abundant chemically diverse organic and inorganic compounds exhibiting a range of biological functions from antioxidant properties to antibacterial and anticancer capabilities, has garnered significant attention. Sulphur nanoparticles (SNPs) find widespread application in diverse fields such as lithium sulfur batteries, sulphur-based photocatalysts, and antimicrobial agents. Despite their extensive utilization in non-biomedical domains, such as drug delivery and cancer prevention strategies, SNPs face challenges when employed for biomedical purposes. Concerns include toxicity, limited reactivity, and the substantial particle size of SNPs, which hinder their effectiveness as drug delivery carriers. To overcome these obstacles, surface modifications of SNPs are necessary to enhance their biomedical applicability.

A Review on Head-Related Transfer Function Generation for Spatial Audio

A head-related transfer function (HRTF) is a mathematical model that describes the acoustic path between a sound source and a listener’s ear. Using binaural synthesis techniques, HRTFs play a crucial role in creating immersive audio experiences through headphones or loudspeakers, using binaural synthesis techniques. HRTF measurements can be conducted either with standardised mannequins or with in-ear microphones on real subjects. However, various challenges arise in, for example, individual differences in head shape, pinnae geometry, and torso dimensions, as well as in the extensive number of measurements required for optimal audio immersion. To address these issues, numerous methods have been developed to generate new HRTFs from existing data or through computer simulations. This review paper provides an overview of the current approaches and technologies for generating, adapting, and optimising HRTFs, with a focus on physical modelling, anthropometric techniques, machine learning methods, interpolation strategies, and their practical applications.

Opening the AI Black Box: Distilling Machine-Learned Algorithms into Code

Can we turn AI black boxes into code? Although this mission sounds extremely challenging, we show that it is not entirely impossible by presenting a proof-of-concept method, MIPS, that can synthesize programs based on the automated mechanistic interpretability of neural networks trained to perform the desired task, auto-distilling the learned algorithm into Python code. We test MIPS on a benchmark of 62 algorithmic tasks that can be learned by an RNN and find it highly complementary to GPT-4: MIPS solves 32 of them, including 13 that are not solved by GPT-4 (which also solves 30). MIPS uses an integer autoencoder to convert the RNN into a finite state machine, then applies Boolean or integer symbolic regression to capture the learned algorithm. As opposed to large language models, this program synthesis technique makes no use of (and is therefore not limited by) human training data such as algorithms and code from GitHub. We discuss opportunities and challenges for scaling up this approach to make machine-learned models more interpretable and trustworthy.

High temperature capture of CO2 on Li4SiO4 sorbents via a simple dry ball-milling coupled with K2CO3 physical addition

The reversible CO2 absorption/desorption of lithium orthosilicate (Li4SiO4) sorbents holds potential for high temperature capture of CO2 from hot flue gases, sorption-enhanced reforming and solar thermochemical energy storage. In this study, we have prepared a series of Li4SiO4 sorbents using a combination of K2CO3 addition and dry ball-milling procedure to improve the relatively slow kinetics under low CO2 partial pressure conditions. The synergistic effects of dry ball-milling and K2CO3 addition on the intrinsic properties of Li4SiO4 sorbents were explored by thermogravimetric analysis and structural characterizations. Thermogravimetric analysis indicate that the highest CO2 uptakes were achieved with dry ball-milling combined with K2CO3 physical addition. The structural characterizations further reveal that this sorbent (P-3K-1.5 M) had the smallest crystallite/particle size, largest surface area, and highest availability of surface alkaline-sites. The kinetics analysis also demonstrates that P-3K-1.5 M exhibited the fastest sorption kinetics during a double process. Additionally, P-3K-1.5 M maintained a high capacity over 10 sorption/desorption cycles. Therefore, this synthesis technique, which is simple, cost-effective, and easily scalable, shows great promise for high-temperature CO2 capture.

Sustainable public policy instruments: revealing global interest in circular economy and eco-innovations

The concept of eco-innovation has become increasingly popular as the industrial industry should move toward sustainability. For businesses to establish core competences in the circular economy model, eco-innovation strategies are crucial components. Academicians are now studying the information behind these corporate decisions, with a growing body of work on the topic. The main goal of the study is to reveal and describe the development and interest in the topic of ecological and environmental innovations through bibliometric analysis. The bibliometric analysis is used to reveal the prevailing trends in the fields of sustainability, sustainable development, green economy, and eco-innovations. The fundamental techniques of comparison, induction, synthesis, analysis, and deduction are applied. In addition, the authors focused on bibliometric analysis, as it allows the processing of huge amounts of data and the creation of scientific maps. The development process in various countries around the world is diverse. Environmental and ecological innovations appear to present a substantial challenge. The prioritisation of the circular economy is also observable in scientific publications and in the academic sphere. Bibliometric analysis reveals the increasing popularity of the circular economy and ecoinnovations among authors, indicating the unavoidable need to tackle a more sustainable approach to economic and production activities.

Impregnated layer solution combustion synthesized CaO-based composite pellets with enhanced CO2 sorption property

The impregnated layer solution combustion synthesis (SCS) technique was employed to synthesize CaO-based sorbents supported by inert stabilizers so as to improve the cyclic CO2 uptake stability. Four different inert stabilizers were evaluated for their impact on the cyclic CO2 uptake stability of sorbents. It found that the Mg-based inert stabilizer significantly outperforms the other three in improving cyclic CO2 sorption capabilities of sorbents synthesized by the impregnated layer SCS method. Moreover, MgO-supported sorbent pellets were prepared via graphite hydrophobic layer-assisted casting, with the magnesia load within the pellets being regulated between 15 and 45 wt %. It is the addition of MgO stabilizer that significantly improves the performance of CaO-based sorbents, with the improvements being notably more pronounced at higher MgO loads. The calcium-based sorbent pellets containing 45 wt % MgO show extremely stable CO2 sorption performance; after 17 cycles, the carbonation conversion rate is 73.6%, about three times the rate of pellets without MgO loading. The uniform dispersion of MgO inert stabilizer inside pellets improves high-temperature sintering resistance and, concurrently, enhances the mechanical property. Furthermore, the use of waste cigarette butts as impregnated layer templates, rather than other organic additives, will significantly reduce the cost of synthesizing MgO-supported CaO-based sorbents.Graphical

Efficient hybrid supercapacitor performance enabled by large surface area of 2D mesoporous zinc sulfide nano-sheets synthesized via microwaves

Researchers have shown a significant amount of interest in synthesizing high energy density supercapacitors using a simplest, fast, and low cost technique. The electrochemical performance of supercapacitors can be impacted by the surface area and morphology of electrode materials. A one-step, rapid, and economical microwave-assisted synthesis technique was employed in this study in order to prepare mesoporous nanosheets that are composed of zinc sulfide. The ZnS-based nanosheets possess a large surface area of ∼120 m2g−1 and a mesoporous structure of a pore diameter of <22 nm, which offers numerous electrochemical active sites and it facilitates an excellent super capacitive performance, which is due to its shortened ion/electron diffusion path. The prepared mesoporous nanosheets exhibit a higher specific capacitance of 2282 Fg−1 (1037 C/g) when subjected to a 1 Ag−1 in 2 M KOH aqueous electrolyte with high capability rate. The fabricated device exhibits a high specific capacitance of 252.5 Fg−1 (140 C/g) at 1 Ag−1, which produces a remarkable energy density of about 90 Whkg−1 at 800 Wkg−1 value of power density and an excellent retention of ∼95 % after 10,000 cycles at 6 Ag−1. This study designed an instant, straightforward and low-cost approach to fabricate ZnS nanosheet electrode materials that exhibit excellent performance for supercapacitor applications.

Ultrafast synthesis of highly graphitized carbon foams through water explosion method

In recent years, carbon foams have attracted considerable attention due to their distinctive physical and chemical properties, including high specific surface area, excellent electrical conductivity, and robust chemical stability, which render them highly suitable for applications in energy storage, catalysis, and adsorption. However, conventional carbon foams are limited by low levels of graphitization and a lack of long-range structural order, restricting their use in high-performance applications. Traditional synthesis methods, such as templating, chemical activation, and hydrothermal processes, although effective in forming porous structures, are complex and inefficient. To overcome these limitations, this study introduces a novel one-step water explosion method that combines Joule heating and steam activation to synthesize highly graphitized porous carbon foam. By rapidly vaporizing intercalated water molecules between graphite layers, the method overcomes van der Waals forces, leading to the exfoliation of the material. The process, conducted under ultra-high temperatures in less than 1 ​s, produces carbon foam with high porosity, large surface area, and excellent electrochemical performance. In lithium-ion battery tests, the carbon foam exhibited a high capacity of 516.2 mAh/g at 0.1 A/g and retained 92.77 ​% of its capacity after 1100 cycles. This efficient and scalable synthesis technique offers a promising pathway for the development of advanced anode materials in energy storage applications, significantly outperforming conventional graphite-based materials.

Structure patterns of one-step synthesis of CuNi nanopowders in air environment: Experiment and atomistic simulations

A possibility for one-step synthesis of bimetallic CuNi nanopowders in a different ratio of Ni to Cu by solution combustion synthesis technique under normal air atmosphere without any post reduction is reported. The effect of different types of fuels like citric acid and glycine on the combustion process and characteristics of resultant solid products were investigated. XRD results showed the existing of CuNi as a main phase and small amounts of CuO and (Ni,Cu)4N. Determined CuNi particle sizes were in the range of up to 50 nm. Computer simulation was performed using the molecular dynamics method for similar concentration compositions, but in size range of 4.5–5.5 nm, as a result of cooling the system from 1700 K to 300 K. In addition, two types of melting scenario of binary CuNi NPs were studied: 1) heterogeneous melting of monocrystalline Cu and Ni NPs; 2) melting of the crystallization products of binary NPs. Melting temperatures weakly depend on the choice of the above-mentioned melting scenario. However, the nature of subsequent crystallization can be influenced by the initial energy of the system, which is higher for case 1. The characteristic temperatures of phase transitions of melting and crystallization are determined based on the analysis of hysteresis loops of the specific potential part of the internal energy of NPs. The patterns of atomic and structural segregation in binary CuNi NPs were studied.

Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models

Modern spectroscopic surveys can only target a small fraction of the vast amount of photometrically cataloged sources in wide-field surveys. Here, we report the development of a generative artificial intelligence (AI) method capable of predicting optical galaxy spectra from photometric broadband images alone. This method draws from the latest advances in diffusion models in combination with contrastive networks. We pass multiband galaxy images into the architecture to obtain optical spectra. From these, robust values for galaxy properties can be derived with any methods in the spectroscopic toolbox, such as standard population synthesis techniques and Lick indices. When trained and tested on 64 × 64 pixel images from the Sloan Digital Sky Survey, the global bimodality of star-forming and quiescent galaxies in photometric space is recovered, as well as a mass–metallicity relation of star-forming galaxies. The comparison between the observed and the artificially created spectra shows good agreement in overall metallicity, age, Dn4000, stellar velocity dispersion, and E(B − V) values. Photometric redshift estimates of our generative algorithm can compete with other current, specialized deep learning techniques. Moreover, this work is the first attempt in the literature to infer velocity dispersion from photometric images. Additionally, we can predict the presence of an active galactic nucleus up to an accuracy of 82%. With our method, scientifically interesting galaxy properties, normally requiring spectroscopic inputs, can be obtained in future data sets from large-scale photometric surveys alone. The spectra prediction via AI can further assist in creating realistic mock catalogs.

An Insight into Common and Advanced Synthesis Methodologies of Acyl Urea Analogs Targeting the CNS

: The acyl urea functionality and related analogs are increasingly used in medicinal chemistry and drug design to prove well-defined pharmacological actions and optimal physicochemical properties. This review paper aimed to summarize information related to synthesis methods of acyl urea derivatives, marketed products, and patents to explore their applications in counteracting CNS-related diseases by multiple targeting. Comprehensive data related to acyl urea and related compounds of the last 15 years is collected from various resources to provide enormous information to the readers. The content of the study uniformly includes considerable evidence linked to the common and unique synthesis techniques of acyl urea from starting materials like benzamide, isocyanates, substituted iodoglycals, 2 chloro nicotinic acid, and methyl hydrazines, as well as patents related to the same. This work describes the reactions of several substrates that can be used to manufacture various acyl urea derivatives. An understanding of patents related to acyl urea analogs with specific targets for CNS action may also help researchers working on additional synthesis of acyl urea derivatives with potential CNS action.

Boron Nitride Nanostructures (BNNs) Within Metal–Organic Frameworks (MOFs): Electrochemical Platform for Hydrogen Sensing and Storage

Boron nitride nanostructures (BNNs), including nanotubes, nanosheets, and nanoribbons, are renowned for their exceptional thermal stability, chemical inertness, mechanical strength, and high surface area, making them suitable for advanced material applications. Metal–organic frameworks (MOFs), characterized by their porous crystalline structures, high surface area, and tunable porosity, have emerged as excellent candidates for gas adsorption and storage applications, particularly in the context of hydrogen. This paper explores the synthesis and properties of BNNs and MOFs, alongside the innovative approach of integrating BNNs within MOFs to create composite materials with synergistic properties. The integration of BNNs into MOFs enhances the overall thermal and chemical stability of the composite while improving hydrogen sensing and storage performance. Various synthesis methods for both BNNs and MOFs are discussed, including chemical vapor deposition, solvothermal synthesis, and in situ growth, with a focus on their scalability and reproducibility. Furthermore, the mechanisms underlying hydrogen sensing and storage are examined, including physisorption, chemisorption, charge transfer, and work function modulation. Electrochemical characterization techniques, such as cyclic voltammetry, electrochemical impedance spectroscopy, and galvanostatic charge–discharge, are used to analyze the performance of BNN-MOF systems in hydrogen storage and sensing applications. These methods offer insights into the material’s electrochemical behavior and its potential to store hydrogen efficiently. Potential industrial applications of BNN-MOF composites are highlighted, particularly in fuel cells, hydrogen-powered vehicles, safety monitoring in hydrogen production and distribution networks, and energy storage devices. The integration of these materials can contribute significantly to the development of more efficient hydrogen energy systems. Finally, this study outlines key recommendations for future research, which include optimizing synthesis techniques, improving the hydrogen interaction mechanisms, enhancing the stability and durability of BNN-MOF composites, and performing comprehensive economic and environmental assessments. BNN-MOF composites represent a promising direction in the advancement of hydrogen sensing and storage technologies, offering significant potential to support the transition toward sustainable energy systems and hydrogen-based economies.