Restricted Capacity Research Articles

Laser-formed nanoporous graphite anodes for enhanced lithium-ion battery performance

Lithium-ion batteries are pivotal in modern energy storage, commonly utilizing graphite anodes for their high theoretical capacity and long cycle life. However, graphite anodes face inherent limitations, such as restricted lithium-ion storage capacity and slow diffusion rates. Enhancing the porosity of graphite and increasing d-spacing in expanded graphite anodes have been explored to improve lithium-ion diffusion and intercalation. Recent advancements suggest that nanoscale modifications, such as utilizing nano-graphite and graphene, can further enhance performance. Laser processing has emerged as a promising technique for synthesizing and modifying graphite and graphene-related materials, offering control over surface defects and microstructure. Here, we demonstrate an industrially compatible one-step laser processing method to transform a nano-graphite and graphene mixture into a nanoporous matrix, significantly improving lithium-ion battery performance. The laser-processed anodes demonstrated significantly enhanced specific capacities at all charge rates, with improved relative performance at higher charge rates. Additionally, long-term cycling at 1 C showed that laser-processed cells outperformed their non-processed counterparts, with specific capacities of 323 and 241 mAh/g, respectively.

Tailoring intervention targeting HPV vaccination in marginalized Roma communities in Slovakia

Abstract Background Marginalized Roma communities (MRCs) are among underserved groups facing limited access to vaccination services and presumably low HPV vaccination rates. The most significant health system barriers to HPV vaccination include financial costs, lack of appropriate information and restricted capacities of healthcare providers. Two interventions identified as promising for implementation in the context of MRCs were discussed with a variety of stakeholders and community members to tailor intervention that would be well accepted and effective to increase HPV vaccination rates in MRCs. Methods The first intervention uses health promoters - trusted and trained community members to address cultural and language barriers and serve as a vital link between healthcare providers and community members. The second multilevel intervention is providing education, navigation, and vaccine access. Initial discussions were conducted with 23 stakeholders, and follow-up discussions were held with 16 stakeholders, including representatives of healthcare professionals, schools, local authorities, and community members. Results An intervention to be implemented in Slovakia focuses on the use of Roma health mediators who will provide parents and vaccine-eligible children (12-14 years old) from MRCs with information on HPV and HPV vaccination, invite them to educational sessions organized in community centres and at schools and navigate them through the vaccination process. The intervention also combines components such as videos and discussions with healthcare professionals, aligning with the needs and wishes of the target group. Conclusions This intervention will help to deliver reliable and understandable information on vaccination and enable people from MRCs to make informed decisions about vaccination. Roma health mediators will take the burden of providing information from overloaded healthcare providers and navigate community members through the vaccination process.

Quantum classical hybrid convolutional neural networks for breast cancer diagnosis.

The World Health Organization states that early diagnosis is essential to increasing the cure rate for breast cancer, which poses a danger to women's health worldwide. However, the efficacy and cost limitations of conventional diagnostic techniques increase the possibility of misdiagnosis. In this work, we present a quantum hybrid classical convolutional neural network (QCCNN) based breast cancer diagnosis approach with the goal of utilizing quantum computing's high-dimensional data processing power and parallelism to increase diagnosis efficiency and accuracy. When working with large-scale and complicated datasets, classical convolutional neural network (CNN) and other machine learning techniques generally demand a large amount of computational resources and time. Their restricted capacity for generalization makes it challenging to maintain consistent performance across multiple data sets. To address these issues, this paper adds a quantum convolutional layer to the classical convolutional neural network to take advantage of quantum computing to improve learning efficiency and processing speed. Simulation experiments on three breast cancer datasets, GBSG, SEER and WDBC, validate the robustness and generalization of QCCNN and significantly outperform CNN and logistic regression models in classification accuracy. This study not only provides a novel method for breast cancer diagnosis but also achieves a breakthrough in breast cancer diagnosis and promotes the development of medical diagnostic technology.

A review on the efficacy of artificial intelligence for managing anxiety disorders.

Anxiety disorders are psychiatric conditions characterized by prolonged and generalized anxiety experienced by individuals in response to various events or situations. At present, anxiety disorders are regarded as the most widespread psychiatric disorders globally. Medication and different types of psychotherapies are employed as the primary therapeutic modalities in clinical practice for the treatment of anxiety disorders. However, combining these two approaches is known to yield more significant benefits than medication alone. Nevertheless, there is a lack of resources and a limited availability of psychotherapy options in underdeveloped areas. Psychotherapy methods encompass relaxation techniques, controlled breathing exercises, visualization exercises, controlled exposure exercises, and cognitive interventions such as challenging negative thoughts. These methods are vital in the treatment of anxiety disorders, but executing them proficiently can be demanding. Moreover, individuals with distinct anxiety disorders are prescribed medications that may cause withdrawal symptoms in some instances. Additionally, there is inadequate availability of face-to-face psychotherapy and a restricted capacity to predict and monitor the health, behavioral, and environmental aspects of individuals with anxiety disorders during the initial phases. In recent years, there has been notable progress in developing and utilizing artificial intelligence (AI) based applications and environments to improve the precision and sensitivity of diagnosing and treating various categories of anxiety disorders. As a result, this study aims to establish the efficacy of AI-enabled environments in addressing the existing challenges in managing anxiety disorders, reducing reliance on medication, and investigating the potential advantages, issues, and opportunities of integrating AI-assisted healthcare for anxiety disorders and enabling personalized therapy.

Activity-based mindfulness: large-scale assessment of an online program on perceived stress and mindfulness.

Mindfulness has emerged as key construct in mental health over past decades. While current mindfulness-based interventions (MBIs) are usually rooted in Asian contemplative traditions, mindfulness practices can equally be found in other knowledge systems, including integrative medicine systems such as anthroposophic medicine (AM). The Activity-Based Stress Release (ABSR) program incorporates the latter as part of an 8-week-long online intervention combining mindfulness exercises, behavioral self-observation, and mindful movement practices derived from this integrative medicine frame. The program could offer additional means for cultivating mindfulness, thereby addressing the necessity for diverse approaches in conjunction with individual differences, diverse clinical demands, or restricted capacities to perform certain mindfulness practices. Using an observational repeated-measures design, the current study aimed to assess a large-scale online implementation of this program in terms of its feasibility, assessing perceived stress and mindfulness. Individuals who enrolled in any of the 37 ABSR program iterations carried out during 2023 and agreed to participate in the study completed online surveys including validated stress and mindfulness scales at the beginning, middle, end, and follow up of the intervention. Linear-mixed models were used for data analysis. A total of 830 individuals took part in the study, of which 53.5% filled in at least 2 surveys. In line with our expectation, mindfulness scores increased significantly over the course of the intervention, while stress scores decreased significantly in this timeframe. We further found differential effects of self-practice frequency and duration on the outcomes. This study provides a first indication of stress reduction in conjunction with the online implementation of this novel MBI. The work further suggests that this AM-based intervention indeed targets mindfulness, as do other MBIs, and that it is adaptable to an online format. However, given the observational single-arm design, controlled studies will be necessary to confirm these results. Nonetheless, the study adds a novel contribution to existent MBIs, which is significant in view of the need for diverse approaches to meet the heterogeneity of individual predispositions and clinical requirements. It remains to established by forthcoming research for which groups of individuals or clinical features this approach could be especially beneficial or less suitable.

Fully automated simplification of urban drainage models on a city scale

ABSTRACT The article presents an innovative method for simplifying urban drainage models. This approach strategically reduces complexity while preserving accuracy in large-scale, high-resolution models such as those of the city of Graz. It involves the selective removal and aggregation of sewer network elements like subcatchments and conduits, ensuring vital features like storage nodes and flow controls remain intact for precise simulations. Despite significant reductions in model components, the simplified version maintains high accuracy in hydrological and hydraulic aspects, including water balance components like infiltration loss, surface runoff, and external outflow. The method proves equally effective across both land cover and sewer shed-based models, offering computational efficiencies that speed-up processing by 20–45 times for the study site. This is particularly beneficial for rapid decision-making and resource optimization in urban planning. The model also adeptly predicts flood events, especially from larger, infrequent rainfall, although an overly restrictive flow capacity can refine flood predictions at the expense of other flow characteristics. Ultimately, this streamlined approach allows for the quick creation of simplified, yet accurate, models from high-resolution city-scale hydrodynamic data or digital twins, facilitating efficient and timely analyses in urban drainage management.

Microfluidic Encapsulation of DNAs in Liquid Beads for Digital Loop‐Mediated Isothermal Amplification

Digital nucleic acid analysis has emerged as a prominent tool for the detection and absolute quantification of diverse pathogens. Digital loop‐mediated isothermal amplification (dLAMP) offers highly sensitive, specific, time‐efficient, and cost‐effective nucleic acid amplification. However, existing dLAMP techniques face challenges such as droplet merging, reliance on surfactants, restricted partition capacities, and the potential for sample loss during heating. Herein, these issues are addressed by introducing liquid beads for sample partitioning. Compared to microwells, our approach overcomes the limitations of chamber dimensions, enabling the analysis of an unlimited number of digitized targets. Furthermore, our novel approach effectively addresses sample loss and merging during thermal processing and eliminates the need for surfactants. Accurate and reproducible the quantitative detection of the gene cluster XALB1 of leaf scald disease is conducted using dLAMP based on liquid beads to verify its availability. The results demonstrate a high correlation between target concentration and positive signals, indicating the robust performance of our technique. A comparative analysis is then performed between dLAMP using liquid beads and using single droplets. Benchmarking these two techniques highlights the effectiveness of our innovative technique in overcoming existing challenges in dLAMP.

SDERP: Stable‐aware differential evolution‐based routing protocol for mobile wireless networks

SummaryA mobile ad hoc network (MANET) is a network with non‐wired communications and non‐centralized administration where all its nodes are mobile. Firstly, one of the leading drawbacks of wireless networks is their energy consumption because the constituent nodes of the network have a restricted battery capacity yet greater consumption caused by the mobility of the nodes, their processing power, the frequent sending of data, and so on. Secondly, data packets may be lost due to traffic, noise, or the mobility of nodes, whose data transmission in real‐time applications would be compromised by this loss. In order to resolve the problem of energy consumption and that of the loss of data packets for MANETs, we propose a new stable‐aware differential evolution‐based routing protocol (SDERP). We include a fitness function to select the best new path from all new paths issued at the end of this algorithm. This fitness function is based on energy constraints and link stability. The performance of our proposal is compared with OPAOMDV‐EE, FT‐AORP, EE‐LB‐AOMDV and AOMDV. The results show that our SDERP has better network performance than the other routing protocols. Specifically, it obtains a packet delivery ratio that is higher by up to 17%, an overhead ratio reduced by up to 33%, an energy efficiency better by up to 39%, and end‐to‐end delay reduced by up to 16%.

Modeling barriers to the adoption of metaverse in the construction industry: An application of fuzzy-DEMATEL approach

The idea of the metaverse has been rather popular in many different sectors since it provides immersive virtual reality where users may connect and participate. Leveraging metaverse technologies—such as virtual reality and augmented reality—for project visualization, collaborative design, and training simulations—in the building industry is attracting increasing attention. Notwithstanding its possible advantages, there is a clear knowledge vacuum on the obstacles preventing the general acceptance of metaverse technology in building. The study intends to close this gap by means of a literature review and analysis of the identified barriers using the fuzzy-DEMATEL technique, therefore separating the causal links among them. There were 26 obstacles found in the literature review and expert comments, arranged technically, organizationally, environmentally, socially, and economically. The most important obstacles shown by results are Security Concerns, Resistance to Change, Lack of Expertise, Siloed Departments, and Training and Education Needs. The results of this research provide building companies and legislators with important new perspectives and direction in developing plans to remove obstacles and encourage the use of metaverse technology. Moreover, the findings of the study provide a road map for industry players in tackling the important issue of restricted data capacities, thereby enabling a better and more successful integration of metaverse technology into building methods.

Impact of the COVID-19 Pandemic on Inpatient Antibiotic and Antifungal Drug Prescribing Volumes in Germany.

Previous studies found that the coronavirus disease 2019 (COVID-19) pandemic had a variable impact on the consumption of antimicrobial drugs in human medicine, with trends in several European countries differing between community and inpatient prescribing. This study analysed changes in the volumes and use density of antibacterial and antifungal drugs dispensed in acute care hospitals in Germany between 2019 and 2022. Surveillance data for the four years available from 279 hospitals were expressed as the total volumes of daily doses or as use density (daily doses per 100 patient/occupied bed days) per year and analysed descriptively, using recommended hospital-adapted daily dose definitions (RDDs) and (as sensitivity analysis) WHO/ATC-defined daily dose definitions (DDD). Hospitals were stratified according to size (number of beds), university affiliation, and location (East, West, South). There were significant decreases in both the total number of patient days and antibacterial drug volumes in 2020 through 2022 compared with 2019. The relative changes between 2019 and 2020, 2021, and 2022 were -12.8%, -13.5%, and -13.3% for patient days, and -9.7%, -11.0%, and -10.1% for antibacterial RDDs, respectively. Broad-spectrum betalactams, notably piperacillin-tazobactam and carbapenems, increased in volume, unlike most other drug classes. The resulting antibacterial drug use density was slightly but significantly increased, with pooled means (and medians) of 43.3 (40.0) RDD/100 in 2019 compared to 44.8 (41.7), 44.5 (40.80), and 44.9 (41.7) RDD/100 in the years 2020 through 2022, respectively. Antifungal drug volumes and use density increased after 2019 and peaked in 2021 (the difference between 2019 and 2021 for total volumes was +6.4%, and that for pooled mean use density values was +22.9%, respectively). These trends were similar in the different hospital strata and comparable when DDDs instead of RDDs were used. Similar to what has been observed in a majority of European countries, the total volume of antibacterial drug use in German acute care hospitals decreased with the pandemic, without a rebound phenomenon in 2022. In association with restricted hospital capacities and presumably more immunocompromised general medicine patients, however, inpatient prescribing of (primarily broad-spectrum) antibacterials and of antifungal drugs increased.

Energy Efficient Approach with ANN based Intrusion Detection System for Securing Network and Route for Transmission in Wireless Sensor Network

The research focuses on hierarchical clustering-based intrusion detection using artificial neural networks (ANNs) for secure data transmission in wireless sensor networks (WSNs). WSNs consist of numerous tiny sensor nodes deployed to monitor environmental phenomena. These nodes face significant challenges, including restricted energy, memory space, communication range, and limited capacity for managing energy, storing, transmitting, and processing data. To address these limitations, a machine learning-based approach is proposed to detect intrusions and efficiently utilize energy by properly selecting cluster heads using a secure clustering protocol. The proposed method was implemented and tested using MATLAB software, employing the NSLKDD and UNSW-NB15 datasets for intrusion detection. The results demonstrated promising outcomes in detecting intruders and enhancing network efficiency, achieving a 92% packet delivery ratio (PDR) and 1.82 Mbps throughput. The study concludes that while WSNs are gaining popularity due to their simplicity, flexibility, and scalability, innovative solutions are necessary for efficient energy management and security. Future research should focus on advanced machine learning models, energy harvesting techniques, scalable protocols, real-time data processing, and integration with IoT platforms for broader applications and enhanced functionality.

Machine Learning based Algorithm Selection and Genetic Algorithms for serial-batch scheduling

Whenever combinatorial optimization problems cannot be solved by exact solution methods in reasonable time, tailor-made algorithms (heuristics, meta-heuristics) are developed. Often, these heuristics exploit structural properties and perform well on selected subsets of the problem space. For example, this is how the two best-known construction heuristics solve the scheduling problem investigated in this study (i.e., the scheduling of parallel serial-batch processing machines with incompatible job families, restricted batch capacities, arbitrary batch capacity demands, and sequence-dependent setup times). However, when the properties change, the performance of one algorithm might decrease, and another algorithm might have been the better choice. To resolve this issue, we propose using Machine Learning to exploit the strengths of different algorithms and to select the probably best-performing algorithm for each problem instance individually. To that, we investigate a variety of methods from the “learning-to-rank” literature and propose several adaptations. Furthermore, because there is no algorithm for the considered scheduling problem that is capable to explore the entire solution space, we developed two Genetic Algorithms for the improvement of initial solutions computed by the selected algorithms. Here, we put special emphasis on ensuring that the solution representation (encoding) reflects the entire solution space and that the operators (e.g., for recombination and mutation) are appropriate to explore and exploit this space completely. Our computational experiments show an average increase of 39.19% in solution quality.

Selectively recovering rare earth elements with carboxyl immobilized metal-organic framework from ammonium-rich wastewater

The material with high adsorption capacity and selectivity is essential for recovering rare earth elements (REES) from ammonium (NH4+-N)-rich wastewater. Although the emerging metal-organic framework (MOF) has gained intensive attention in REES recovery, there are scientific difficulties unsolved regarding restricted adsorption capacity and selectivity, hindering its extensive engineering applications. In this work, a diethylenetriamine pentaacetic (DTPA)-modified MOF material (MIL-101(Cr)-NH-DTPA) was prepared through an amidation reaction. The MIL-101(Cr)-NH-DTPA showed enhanced adsorption capacity for La(III) (69.78 mg g−1), Eu(III) (103.01 mg g−1) and Er(III) (83.41 mg g−1). The adsorption isotherm and physical chemistry of materials indicated that the adsorption of REEs with MIL-101(Cr)-NH-DTPA was achieved via complexation instead of electrostatic adsorption. Such complexation reaction was principally governed by -COOH instead of -NH2 or -NO2. Meanwhile, the resulting material remained in its superior activity even after five cycles. Such a constructed adsorbent also exhibited excellent selective adsorption activity for La(III), Eu(III), and Er(III), with removal efficiency reaching 70% in NH4+-N concentrations ranging from 100 to 1500 mg L−1. This work offers underlying guidelines for exploitation an adsorbent for REEs recovery from wastewater.

Engineering of Graphitic Carbon Nitride (g-C3N4) Based Photocatalysts for Atmospheric Protection: Modification Strategies, Recent Progress, and Application Challenges.

Graphitic carbon nitride (g-C3N4) is a prominent photocatalyst that has attracted substantial interest in the field of photocatalytic environmental remediation due to the low cost of fabrication, robust chemical structure, adaptable and tunable energy bandgaps, superior photoelectrochemical properties, cost-effective feedstocks, and distinctive framework. Nonetheless, the practical application of bulk g-C3N4 in the photocatalysis field is limited by the fast recombination of photogenerated e--h+ pairs, insufficient surface-active sites, and restricted redox capacity. Consequently, a great deal of research has been devoted to solving these scientific challenges for large-scale applications. This review concisely presents the latest advancements in g-C3N4-based photocatalyst modification strategies, and offers a comprehensive analysis of the benefits and preparation techniques for each strategy. It aims to articulate the complex relationship between theory, microstructure, and activities of g-C3N4-based photocatalysts for atmospheric protection. Finally, both the challenges and opportunities for the development of g-C3N4-based photocatalysts are highlighted. It is highly believed that this special review will provide new insight into the synthesis, modification, and broadening of g-C3N4-based photocatalysts for atmospheric protection.

Дорожная карта по обеспечению бесперебойной связи посредством интеграции сетей 5G и радиодоступа

Background: Due to the rising prevalence of smart devices and the need for dependable, high-speed internet connections, it is imperative to enhance existing network infrastructures. The fundamental framework of mobile telephony, referred to as RAN (Radio Access Network), needs help fulfilling these requirements due to its restricted capacity and concerns over scalability. Objective: The purpose of this research is to investigate the capabilities of 5G technology in addressing these restrictions, with a specific focus on integration techniques, advantages, and obstacles. Methodology: The study used a mixed-method approach to assess the efficacy and feasibility of incorporating 5G-RAN. This is achieved by integrating quantitative data from pilot studies with qualitative literature analysis. Results: The findings suggest that using 5G technology results in substantial improvements in data transfer rates, reduced latency, enhanced network reliability, scalability, and lowered latency. However, several obstacles have been identified, including the significant costs linked to infrastructure, issues around interoperability, and security concerns. Conclusion: Integrating 5G and RAN provides a feasible approach to achieving uninterrupted connectivity, leading to significant advantages in many industries. However, overcoming the challenges above is essential to ensure this technology's effective implementation and general acceptance.

PODI: A Private Object Detection Inference framework for autonomous vehicles

Object detection is essential for autonomous vehicles to perceive and understand their environment. The restricted storage and processing capacities of vehicles necessitate the outsourcing of object detection services. However, this may raise concerns regarding the privacy of the uploaded images. Although there have been some studies on privacy-preserving object detection networks, they either lack location privacy protection or involve excessive computational and communication overheads. To address this issue, we propose a private object detection inference framework (PODI), which is based on a Faster R-CNN and aims to protect both classification and location privacy. PODI employs additive secret sharing protocols to support collaborative computation between two edge servers. By using efficient protocols such as secure Maxpool, secure array access, and secure exponent, PODI significantly reduces computational and communication overheads. A theoretical analysis has confirmed the security, correctness, and efficiency of PODI. Extensive experiments were used to demonstrate its security, inference accuracy comparable to plaintext approaches, and lower cost of secure inference.

Sunlight-powered sustained flight of an ultralight micro aerial vehicle.

Limited flight duration is a considerable obstacle to the widespread application of micro aerial vehicles (MAVs)1-3, especially for ultralightweight MAVs weighing less than 10 g, which, in general, have a flight endurance of no more than 10 min (refs. 1,4). Sunlight power5-7 is a potential alternative to improve the endurance of ultralight MAVs, but owing to the restricted payload capacity of the vehicle and low lift-to-power efficiency of traditional propulsion systems, previous studies have not achieved untethered sustained flight of MAVs fully powered by natural sunlight8,9. Here, to address these challenges, we introduce the CoulombFly, an electrostatic flyer consisting of an electrostatic-driven propulsion system with a high lift-to-power efficiency of 30.7 g W-1 and an ultralight kilovolt power system with a low power consumption of 0.568 W, to realize solar-powered sustained flight of an MAV under natural sunlight conditions (920W m-2). The vehicle's total mass is only 4.21 g, within 1/600 of the existing lightest sunlight-powered aerial vehicle6.

Strategic behavior in TSO-DSO coordinated flexibility markets: A Nash equilibrium and efficiency analysis

This paper investigates the way in which the design of a TSO-DSO coordinated flexibility market can enable strategic behavior by flexibility service providers (FSPs). Multiple flexibility market models are considered for the procurement of flexibility services by transmission and distribution system operators, namely: a common (joint) market, a fragmented market, and a sequential multi-level market. Considering these market models, three non-cooperative games are introduced to investigate the strategic bidding and interaction between FSPs therein. Detailed conclusions are then drawn on the existence and uniqueness of Nash Equilibria (NEs) in the developed games, including derivations of closed-form expressions of the resulting NEs and corresponding price-of-anarchy, capturing the FSPs’ strategic bidding impact on the markets’ efficiency. The analysis considers – first in a duopoly setting, then with multiple players – three different use cases representing when: (1) a sufficient flexible capacity exists (sufficient flexibility offered from the FSPs and adequate interconnection/grid capacity between systems); (2) participants have a scarce flexibility capacity; and (3) a restrictive interface capacity exists between the systems. A case study considering an interconnected transmission–distribution system and multiple FSPs corroborates the analytical findings. The obtained results show that market participants have incentives to set bid prices greater than their marginal costs, thus decreasing the markets’ efficiency. This aspect is shown to be more pronounced when the available flexible capacity is limited, a restrictive line limit is present, or when the market is fragmented, thus supporting the need for additional network investments and the creation of joint flexibility market formats.

Shadows of quantum machine learning

Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a major obstacle to the widespread use of quantum machine learning models in practice is that these models, even once trained, still require access to a quantum computer in order to be evaluated on new data. To solve this issue, we introduce a class of quantum models where quantum resources are only required during training, while the deployment of the trained model is classical. Specifically, the training phase of our models ends with the generation of a ‘shadow model’ from which the classical deployment becomes possible. We prove that: (i) this class of models is universal for classically-deployed quantum machine learning; (ii) it does have restricted learning capacities compared to ‘fully quantum’ models, but nonetheless (iii) it achieves a provable learning advantage over fully classical learners, contingent on widely believed assumptions in complexity theory. These results provide compelling evidence that quantum machine learning can confer learning advantages across a substantially broader range of scenarios, where quantum computers are exclusively employed during the training phase. By enabling classical deployment, our approach facilitates the implementation of quantum machine learning models in various practical contexts.

Stearic acid enhanced starch nanocrystal integration in cassava starch film

SummaryStarch nanocrystals (SNCs) have emerged as promising biodegradable nanomaterial for the enhancement of biopolymer films' properties. Nevertheless, using SNCs alone is difficult due to restricted capacity for nanocrystal integration within the film's matrix. The objective of this study was to enhance the properties of biopolymer films using waxy corn starch nanocrystals supplemented with stearic acid. Cassava starch films containing varying proportions of SNCs (0–20 wt.% starch) and steric acid (2%) were prepared and characterised. The inclusion of stearic acid substantially improved SNC integration from 5% to 15%, yielding nanocomposite films with better tensile strength, thermal stability and barrier properties than those prepared with native starch or starch‐fatty acid alone. The improvement in film properties was attributed to V‐amylose complexes formation, increased crystallinity and hydrophobicity. Microscopy images revealed homogenous film surfaces, depicting greater nanocrystal dispersibility. The supplementation of lipid additives offers a valuable strategy to improve the integration of SNCs in film matrices.