Industrial Benefits Research Articles

Latest Trend and Challenges in Machine Learning– and Deep Learning–Based Computational Techniques in Poultry Health and Disease Management: A Review

To determine the flock’s economic worth, free‐range chicken growers must determine the gender, bird movement, behavior, disease detection, and lameness of the chickens. However, because of the complex environmental background and the fluctuating chicken population, it is difficult for farmers to effectively and properly measure those characteristics. Manual estimation is also inaccurate and time‐consuming because probable identification occurs in their life cycle. Therefore, the industry benefits from automated systems that can produce findings quickly and precisely in managing health and diseases. The advancement of machine learning (ML)– and deep learning (DL)–based algorithms are boons for poultry health and disease management. This study reviews the literature using ML and DL techniques in prediction, classification, and disease detection in various metrics, namely, poultry health and disease management. We have considered the research article published from 2010 to 2023 in this study, which uses ML‐ and DL‐based computation techniques in poultry welfare metrics such as gender identification, tracking of poultry, analysis of broiler chicken behavior, detection of poultry diseases, lameness and broiler weight, and stress monitoring. In addition, this review explores the most recent developments, difficulties, strategies, and databases used in image preprocessing feature extraction and classification. The review addresses these challenges and discusses the approaches and techniques researchers employ to tackle them in the field of poultry management and disease detection.

PRINCIP ZERO WASTE U FUNKCIJI POVEĆANJA EKONOMSKOG RAZVOJA U HOTELSKOM POSLOVANJU

The aim of this research is to determine the impact of implementing the Zero Waste strategy on economic benefits in the hotel industry of the Republic of Serbia. A quantitative methodology was employed, comprising a survey among managers from various hotels, combined with statistical data analysis to verify hypotheses. The results indicate that investments in employee education, effective waste management policies, and investments in sustainable technologies significantly contribute to profit enhancement. This research contributes to the theoretical understanding and practical application of the Zero Waste strategy in the hospitality sector, providing evidence supporting the economic and environmental value of sustainable practices.

A Metaheuristic Framework for Energy-Intensive Industries With Batch Processing Machines

Batch processing machines, which operate multiple jobs at a time, are commonly used in energy-intensive industries. A significant amount of energy can be saved in such industries using production scheduling as an approach to enhance efficiency. This study deals with an energy-aware scheduling problem for parallel batch processing machines with incompatible families and job release times. In such an environment, a machine may need to wait until all the jobs in the next batch become ready. During waiting time, a machine can be switched <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> or kept <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> standby for more energy-efficient scheduling. We first present a mixed-integer linear programming (MILP) model to solve the problem. However, the presented MILP model can only solve small problem instances. We therefore propose an energy-efficient tabu search (ETS) algorithm for solving larger problem instances. The proposed solution framework incorporates multiple neighborhood methods for efficient exploration of the search space. An energy-related heuristic is also integrated into the ETS for minimizing energy consumption during the waiting time. The performance of our proposed ETS algorithm is validated by comparing it with CPLEX for small problem instances and with two other heuristic algorithms for larger problem instances. The contribution of different components in ETS is also established in our experimental studies. The proposed solution framework is expected to bring many benefits in energy-intensive industries both economically and environmentally.

Mixed Culture Cultivation in Microbial Bioprocesses.

Mixed culture cultivation is well renowned for industrial applications due to its technological and economic benefits in bioprocess, food processing, and pharmaceutical industries. A mixed consortium encompasses to achieve growth in unsterile conditions, robustness to environmental stresses, perform difficult functions, show better substrate utilization, and increase productivity. Hence, mixed cultures are being valorized currently and has also augmented our understanding of microbial activities in communities. This chapter covers a wide range of discussion on recent improvements in mixed culture cultivation for microbial bioprocessing and multifarious applications in different areas. The history of microbial culture, microbial metabolism in mixed culture, biosynthetic pathway studies, isolation and identification of strains, along with the types of microbial interactions involved during their production and propagation, are meticulously detailed in the current chapter. Besides, parameters for evaluating mixed culture performance, large-scale production, and challenges associated with it are also discussed vividly. Microbial community, characteristics of single and mixed culture fermentation, and microbe-microbe interactions in mixed cultures have been summarized comprehensively. Lastly, various challenges and opportunities in the area of microbial mixed culture that are obligatory to improve the current knowledge of microbial bioprocesses are projected.

MICE tourism, conferences, festivals, globalization, tourism infrastructure

This article discusses the prospects for the development of MICE tourism in the Republic of Kazakhstan. Due to its strategic location in Central Asia and growing infrastructure, Kazakhstan is positioning itself as a promising destination for international events. The study analyzes the current state of the MICE industry in the country, examines strengths, weaknesses and opportunities using SWOT analysis. The article highlights key issues such as limited global awareness, lack of qualified personnel and competition from neighboring countries. The paper presents recommendations for improving Kazakhstan's competitiveness in the MICE sector, including strategic marketing, international cooperation and public-private partnerships. The results obtained indicate that, when implementing targeted strategies and investments, Kazakhstan has significant potential to become a major player in the global MICE industry, offering economic and social benefits.

Artificial Intelligence in the Printing Industry: A Systematic Review of Industrial Applications, Challenges and Benefits

The commercial printing industry is undergoing a significant transformation with the integration of Artificial Intelligence (AI), yet there is limited literature on its specific applications and impact. This systematic review addresses two critical research questions: What are the key industrial domains where AI is applied in the printing industry, and what are the associated benefits and challenges? Our study identifies the primary AI applications across Production Planning and Control (PPC), Quality Management (QM), Maintenance Management (MM), and Supply Chain Management (SCM). PPC employs optimization algorithms to optimise scheduling and resource allocation, improving production efficiency. In QM, Machine Learning and Computer Vision detect defects and optimize print quality. MM focuses on minimizing downtime by ensuring machines operate efficiently through predictive maintenance. Despite these advancements, challenges such as high-quality data requirements, algorithmic complexity, and integration difficulties persist. This research fills a critical gap in the literature by providing a comprehensive overview of AI’s role in the printing industry, offering valuable insights into its potential to drive efficiency, cost savings, and quality improvements. Our findings suggest that while AI holds substantial promise, its benefits are contingent upon overcoming significant implementation challenges. This study contributes to the ongoing discourse on digital transformation by providing a robust framework for understanding AI’s current and future impact on the printing sector. Our methodology followed PRISMA guidelines, with a thorough review and thematic analysis of peer-reviewed studies. The insights gained from this review can guide future research and practical implementations, positioning AI as a crucial enabler in the evolving landscape of the printing industry.

Comparative Analysis of Big Data Computing in Industry 4.0 and Industry 5.0: An Experimental Study

A comparison of the use of big data computing in Industry 4.0 and Industry 5.0 was carried out utilizing data collected from the actual world for the purpose of this research. The findings suggest that there has been a 2% drop in the number of faulty items produced in Industry 5.0, coupled with a 1% decrease in the amount of energy used in highly automated companies. According to the findings of the quality control, fault Type B accounts for around 65 percent of the overall defects in Industry 4.0. The results highlight the benefits of Industry 5.0, which capitalizes on human-machine cooperation, data-driven processes, and customized products and services. These insights help to contribute to manufacturing processes that are more efficient, more sustainable, and more quality-driven. Big data computing, Industry 4.0 and 5.0, quality control, and energy efficiency are some of the keywords to look for.

Assessing Factory's Industry 4.0 Readiness: A Practical Method for IIoT Sensor and Network Analysis

Manufacturing industries are aware of the benefits of Industry 4.0 (I4.0) and the pivotal role of the Industrial Internet of Things (IIoT) to facilitate the transition. However, they often underestimate the intricate connection between the implementation of IIoT and their existing sensing and communication systems. Hindering their progress towards I4.0 implementation, many factories still rely on rigid sensing systems, characterized by limited adaptability and inflexibility. This raises questions regarding the industrial readiness for I4.0 implementation and its ability to meet the sensing and connectivity standards of IIoT. Specifically, it is unclear whether the current brownfield installations, consisting of sensors and networking systems, adequately support the demanding I4.0 applications that necessitate a multi-functional and collaborative IIoT system. Our objective is to assess a factory's progress towards I4.0 by examining the readiness of its sensing system and the communication capabilities of its network. We propose a two-stage analysis of factory readiness, including a functional segment-based sensor analysis and an application class-based network analysis. We present a case study conducted at an iron-making plant in The Netherlands to illustrate our method. Key findings of the case study include: (1) a lack of multi-functionality across segments for the majority of sensors (90%), (2) a considerable portion of network traffic (73%) requires high reliability, and (3) only 3% of the current network traffic necessitate ultra-reliable, low latency communication. Furthermore, we discuss how our method provides decision-makers with valuable guidance for the digital transformation of established and newly built manufacturing industries.

Synergistic optimization of rural green eco-tourism industry chain under the support of information technology

Abstract At present, the full development of tourism and other tertiary industries has gradually become the economic driving force of the countryside, but the negative impact on the countryside environment is becoming more and more prominent. Therefore, achieving the synergy optimization of rural green ecology and the tourism industry is a problem that needs to be solved. In this paper, starting from the rural green ecological tourism information, through the analysis of the basic elements of tourism information and the sampling of tourism information data, we construct a data feature extraction sampling model based on distributed detection, which is used for the reconstruction of the collection of tourism information data to ensure the effectiveness of information processing. Utilizing the method of fuzzy clustering, mining tourism information data, establishing the data distribution function, and collecting tourism information data features. The model is applied in practice to predict the level of tourist consumption and the daily flow of tourists in tourist attractions, and the results show that the monthly consumption of tourists in the year of the study area is 44.08 yuan less than that of the previous year, so it is necessary to adjust the tourism policy to stimulate consumption. In the coupling coordination degree of informationization and tourism industry development, it is found that the index of informationization application level has a faster rate of improvement than other indexes, with an increase of 546.09% between 2017-2018, and the index data of industrial benefits basically achieve an annual increment, reaching 0.4215 in 2021.

O raro NARC-G1 alho: quantificação comparativa de alicina com perfil morfo-bioquímico e genético

Garlic (Allium sativum) is an important cash food crop, and the biotechnology industry has considerable interest in the plant because of its medicinal importance. These medicinal properties are attributed to organosulphur compounds as the accumulation of these compounds varies according to genotype, locality, light quality, and cultivation practices. In this study, we compared a newly developed garlic variety NARC-G1 by National Agricultural Research Centre (NARC), Islamabad, Pakistan with three different garlic cultivars and highlighted the distinctive attributes like phenotypic characteristics, the content of allicin, elemental profile, and gene polymorphism. Phenotypic analysis showed NARC-G1 has significantly higher bulb weight (66.36g ± 18.58), single clove weight (5.87g ± 1.041), and clove width (17.41mm ± 0.95) which directly correlates to the size of the garlic. The analytical analysis showed the highest allicin content (4.82 ± 0.001) in NARC-G1. Genotyping of the alliinase in all four cultivars showed indels in the gene resulting in distinguishable changes in organosulphur compounds' profile. NARC-G1 is unique from other garlic cultivars and could be the best choice for mass production with proper cultivation and irrigation management. Moreover, for Pakistan NARC-G1 could be a potential contender to earn the industrial benefits with inland cultivation instead of importing garlic alleviating the economic burden.

Degradation potential of various enzymes in bioremediation of toxic contaminants

Bioremediation is term define to utilise different living organisms for reducing the toxic impacts of contaminants. There are numerous of contaminants in the environment with their persistence and bioaccumulation abilities. These contaminants involved heavy metals, dye disposals, petroleum wastes, oil spills etc. Enzymes are biocatalysts that are responsible for hydrolysing a particular reaction in a specific cell of living organisms. Enzymes have been serving in various sectors for several years’ industries like pharmaceuticals, food, detergent, leather, paper, fabric, and many more. Despite various applications of biological or microbial enzymes, they are comparatively cheaper, stable, and reusable than chemical catalysts. Therefore enzyme-based reactions are preferable to chemical reactions in almost every industry. Proteases, amylases, and lipases are prioritized in the list of industrial enzymes cellulases. Microbial enzymes are attractive to researchers because of their high yield in comparison to chemical reactions. Enzyme applications are not restricted to industrial benefits but they are more eco-friendly than other chemical biocatalysts for example enzymes can reduce dye contaminants from the environment. Over chemical reactions, enzymes are the most suited replaceable candidate due to their agricultural significance. Hence this study will be involved in the comparison of biological catalysts so Enzymes are better options instead of chemical components. Therefore, in this review we will discuss different approach for the degradation for various contaminants with microbes.

Ultra-broadband sound absorption in a compact multi-chamber micro-perforated panel absorber with varying depths

This study presents an enhanced multi-chamber micro-perforated panel absorber (MC-MPPA) with varying sub-chamber depths, offering ultra-broadband low-frequency sound absorption. Traditional micro-perforated panel absorbers are constrained by a limited bandwidth, necessitating impossibly small perforations for optimal low-frequency absorption. Our innovative design addresses these constraints with a lightweight, compact panel structure that uses varied chamber depths and unique porosities. Using the two-point impedance method from graph theory, an MC-MPPA was modeled and optimized. Notably, our MC-MPPA test pieces achieved impressive sound absorption coefficients experimentally of over 0.8 in the whole frequency ranges of [397–1000] and [698–1895] Hz. The absorber’s thickness is a mere 47 mm, equivalent to 1/18.2 and 1/10.5 of the sound wavelength at the minimum operational frequency, respectively. Theoretically, with a maximum sub-chamber depth of just 20 mm, average absorption coefficient values of 0.6780 and 0.6483 were observed in [200–3000] and [200–4000] Hz ranges, respectively. Our optimization algorithm permits the definition of practical geometric parameters, promising substantial industrial benefits. The results have been validated theoretically, numerically, and experimentally.

Analysis and Research on Design of Dialect Emoticons from the Perspective of Regional Culture in Yancheng

Abstract In this paper, Yancheng’s local culture is classified through literature research, expert interviews, and popular questionnaires. In the dialect matching of emoticons, the high-frequency text used by Yancheng people is used, and the dialect text ranking table measured by experts is combined to provide the main content for producing and designing emoticons. Based on the analysis model of Yancheng material culture and nonmaterial culture, the image design elements of Yancheng regional emojis with local and high cultural characteristics are extracted from Yancheng regional elements. The Design of emoticons that combine pictures and text emphasizes the expression of awareness, starting with modeling the theme image, color matching, scene props, and so on. The animation emoji industry benefits from this valuable feasibility strategy, and new vitality is added to the development of the animation industry in Yancheng.

How Does E-commerce Industry Benefit from Big Data

In this paper, the benefits of applying big data in the industry of online retailing, with a focus on logistics distribution and precision marketing. The findings indicate that big data plays a crucial role in revolutionizing logistics distribution, enabling e-commerce companies to optimize their inventory management, delivery routes, and customer demand forecasting. This results in cost savings, improved efficiency, and enhanced customer satisfaction. Moreover, big data empowers precision marketing by providing deep customer insights, enabling effective segmentation, supporting dynamic pricing strategies, facilitating continuous optimization, and helping to prevent fraud. The findings also highlight the successful implementation of big data in leading Chinese e-commerce platforms, further emphasizing its benefits. Given that this research mainly centres around logistics distribution and precision marketing, its limitations is crucial to be acknowledged, with potential biases derived from the literature revie. Future research should address these limitations by exploring other aspects of the e-commerce industry, employing primary data collection methods, and investigating ethical implications and emerging technologies. By doing so, consumers can advance the understanding of big data’s role in driving innovation, improving operational efficiency, and fostering sustainable growth in the e-commerce industry.

Effects of Drying Techniques and Different Solvents on Phytochemical Composition and Antioxidant Activity of Carica papaya Seed Extracts: Implications for Industrial Use and Medicinal Benefits

Plant has always been of immense health benefits to humanity because, plant contains different parts that have been found to have various medicinal and industrial values. Therefore, plants play a crucial role in obtaining their most effective extracts for medical and industrial applications. The primary components of these preparations involve the extraction and drying methods. Carica papaya is a plant that is known to have different parts with numerous and unexplored medicinal and industrial benefits. Thus, this study investigated the effects of drying techniques and different extracting solvents on the phytochemical composition and antioxidant activity of Carica papaya seeds extract using standard analytical procedures. The results showed that drying technique has significant effect (p &lt; 0.05) on the phytochemical composition of the C. papaya seeds with air-drying at room temperature having the highest amount of total phenolic compounds, flavonoids, saponin content and antioxidant activity when compared to other techniques. Hexane extracted the highest amount of total phenolic compounds (121.08 mg/GAE/100 g). Diethyl ether extracted the highest amount of total flavonoid and saponin content while polar solvents (aqueous and ethanol) extracts have the highest amount of antioxidant activity. This study showed that air drying at room temperature is a good method of preservation of the phytochemicals present in medicinal plants and that stereochemistry and structural diversity of compounds determine the efficiency of their extraction in any solvent. Furthermore, the highlighted phytochemicals and antioxidant activity of C. papaya seed extracts could be of immense benefits in various industries and for medicinal purposes.

Resilience and Performance of Prefabricated Modular Buildings Against Natural Disasters

Earliest global movement towards modular construction originated as a solution to the sudden housing demand which occurred during events such as British colonization, the California gold rush, the world wars and post war settlement. Present day, modular construction is explored by researchers aiming to maximize from the benefits of Industry 4.0 technology. Buildings of the 21st century frequently face natural disasters such as earthquakes, pandemics, floods, cyclones, and bushfires. This review is developed around recent episodes such as the Covid-19 pandemic which demands design resilience and the intraplate earthquake of Australia, which stresses on the necessity of improved structural performance of modular buildings. To understand the performance of modular buildings against natural disasters, this paper critically reviews recent developments in modular construction research and applications. Through the extensive analysis of literature, this paper identifies future research domains of modular construction that are required to confront natural disasters. The outcomes of this review facilitate timely and sustainable research directives towards resilient modular buildings.

Success and Risks Related to Housing Projects in Pakistan: A Moderating Role of Project Management Risk Management

This study investigates the critical success factors (CSFs) and their interplay with project risks in determining the success of housing projects in Karachi, Pakistan, while considering the moderating role of risk management. Analyzing data from 432 project management professionals within Karachi-based housing societies, the study establishes significant relationships between CSFs and various project success dimensions, including time, cost, quality, project profile objectives, impact, stakeholder satisfaction, and preparation for the future. Additionally, the research reveals that project risks exert substantial influence on these success dimensions. Risk management is a strong moderator, increasing risk variable’s influence on project results. The findings provide insightful information for decision-makers, recommending the creation of sector-specific rules that place a strong emphasis on the highest standards in risk management and project execution. The construction industry benefits from this since it encourages long-term development and leads to better results for projects.

An Overview of the Phytochemical Composition of Different Organs of Prunus spinosa L., Their Health Benefits and Application in Food Industry

The purpose of this study is to analyze prospective approaches that emphasize the beneficial biofunctional and metabolic effects of different anatomic parts of the Prunus spinosa L. on maintaining human health and improving some pathophysiological processes. Our research is based on recent data from literature on the biology and ecology of P. spinosa L., focusing on its bioactive compounds. Based on such bifunctional parameters, we aim to conceptualize path-breaking approaches that have as a goal the incorporation of P. spinosa L. fruits into certain food products to make use of their potential metabolic benefits in cardiovascular pathologies and other disorders that are prevalent at present and respond to nutritional intake of antioxidants. It is well known that dietary interventions allow the search for mechanisms leading to the development of effective nutritional therapies. This review has mainly focused on the identification of bioactive and functional nutrients of P. spinosa L. that can be incorporated into diverse food formulations. This is because nutrition plays a pivotal role in the development, validation, and recommendation of the nutritional composition of food, with demonstrated impacts on metabolic processes in specific diet-related pathologies.

Utilising a hybrid DMAIC/TAM model to optimise annual maintenance shutdown performance in the dairy industry: a case study

PurposeOutput from the Irish Dairy Industry has grown rapidly since the abolition of quotas in 2015, with processors investing heavily in capacity expansion to deal with the extra milk volumes. Further capacity gains may be achieved by extending the processing season into the winter, a key enabler for which being the reduction of duration of the winter maintenance overhaul period. This paper aims to investigate if Lean Six Sigma tools and techniques can be used to enhance operational maintenance performance, thereby releasing additional processing capacity.Design/methodology/approachCombining the Six-Sigma Define, Measure, Analyse, Improve, Control (DMAIC) methodology and the structured approach of Turnaround Maintenance (TAM) widely used in process industries creates a novel hybrid model that promises substantial improvement in maintenance overhaul execution. This paper presents a case study applying the DMAIC/TAM model to Ireland’s largest dairy processing site to optimise the annual maintenance shutdown. The objective was to deliver a 30% reduction in the duration of the overhaul, enabling an extension of the processing season.FindingsApplication of the DMAIC/TAM hybrid resulted in process enhancements, employee engagement and a clear roadmap for the operations team. Project goals were delivered, and original objectives exceeded, resulting in €8.9m additional value to the business and a reduction of 36% in the duration of the overhaul.Practical implicationsThe results demonstrate that the model provides a structure that promotes systematic working and a continuous improvement focus that can have substantial benefits for wider industry. Opportunities for further model refinement were identified and will enhance performance in subsequent overhauls.Originality/valueTo the best of the authors’ knowledge, this is the first time that the structure and tools of DMAIC and TAM have been combined into a hybrid methodology and applied in an Irish industrial setting.

Stabilizing Cobalt-Free Li-Rich Layered Oxide Cathodes By Site-Specific Doping

The limited theoretical capacity of traditional cathode materials solely based on transition metal (TM) redox has been a primary factor for the ceiling placed on the energy density of LIBs. In this regard, Li-rich layered oxides (LLOs) have received a great deal of attention, due to their high reversible capacity (> 250 mAh g-1) and high energy density (900 Wh kg-1), courtesy of the TM redox and the additional high-voltage O2- redox.1, 2 Although the electrochemical performance of Co-free LLOs is significantly less satisfactory, developing Co-free cathodes is inevitably a holy grail owing to cobalt’s economic, ethical, and political issues caused by high-cost toxicity. Therefore, studies on Co-free LLOs are necessary for creating long-term benefits for the cathode industry.Unfortunately, Co-free LLOs suffer from capacity and voltage decay on cycling, which for now obviously limits the materials use outside the academics laboratory. It is well accepted that that performance decay is primarily attributed to the irreversible anionic redox and structural degradation in LLOs. The doping strategy has been widely used to address the voltage decay of LLOs.3 While the dopant is typically introduced to the layered R m component, the lithium-rich component (C2/c) in the LLOs, which is the source of anionic redox, has always been ignored.Our team comprehensively investigates the structural involution of LLOs based on a Co-free Li-rich Mn-based Li1.2Ni0.2Mn0.6O2 (LNMO) cathode using in-operando and ex-situ synchrotron-based characterizations. We learned through mechanistic characterization that the transition from the monoclinic (C2/c) to the hexagonal (R-3m) behaviour is an intrinsic behaviour of LLOs for the first time. According to this structure-function relationship, we introduced a small quantity of one electrochemically active metal, ruthenium, into both R-3m and C2/c components in LNMO and found that the oxygen lattice and structural stability can be significantly improved by the site-specific doping against lithiation and delithiation processes.4 Additionally, reducing the irreversible TM migration is key to advancing the performance of layered oxide cathode materials. Contrary to many previous studies that emphasize suppressing cation migration, our team proposed that reversible tetrahedral-octahedral TM migration helps inhibit the formation of undesirable structural changes and phase transformation. The oxidation state of TM, such as Ni, Mn, and Co, are easily trapped into the octahedral sites in the Li layer due to their high octahedral-site splitting energy when a large amount of Li has extracted from the structure. By introducing Cr into LNMO, we found that Cr can migrate reversibly between the tetrahedral and octahedral sites, which makes Cr-doped LNMO possess significantly enhanced structural stability and thus achieve higher electrochemical performance than LNMO.In all, our research focuses on the site-specific doping of the Co-free LLOs and understanding the structure- and chemistry-function relationship of the LLOs. The findings in our work might provide possible pathways for the future design of high-performance cathode materials for lithium-ion batteries and may also apply to developing cathode materials for other energy storage systems.Reference S. Hu, A. S. Pillai, G. Liang, W. K. Pang, H. Wang, Q. Li and Z. Guo, Electrochem. Energy Rev., 2019, 2, 277-311.M. Han, J. Jiao, Z. Liu, X. Shen, Q. Zhang, H.-J. Lin, C.-T. Chen, Q. Kong, W. K. Pang, Z. Guo, R. Yu, L. Gu, Z. Hu, Z. Wang and L. Chen, Adv. Energy Mater., 2020, 10, 1903634.Y. Fan, W. Zhang, Y. Zhao, Z. Guo and Q. Cai, Energy Stor. Mater., 2021, 40, 51-71.Y. Fan, E. Olsson, G. Liang, Z. Wang, A. M. D'Angelo, B. Johannessen, L. Thomsen, B. Cowie, J. Li, F. Zhang, Y. Zhao, W. K. Pang, Q. Cai and Z. Guo, Angew. Chem. Int. Ed., 2022, DOI: 10.1002/anie.202213806.