Processing Strategies Research Articles

Advanced ternary carbon-based hybrid nanocomposites for electromagnetic functional behavior in additive manufacturing

This study explores the processing and performance of acrylonitrile butadiene styrene (ABS)-based carbon ternary hybrid nanocomposites, incorporating carbon nanotubes (CNT), graphene nanoplatelets (GNP), and carbon black (CB), for applications in electromagnetic compatibility (EMC). The effect of nanocomposite processing on electromagnetic properties was evaluated by varying the mixing protocol, either through direct extrusion with simultaneous addition of all constituents or by preparing a master batch followed by dilution. The impact of nanofiller morphology and processing techniques on the behavior of nanocomposites was systematically investigated. Filaments of these nanocomposites were Additive Manufactured via Material Extrusion, and the resulting parts were evaluated for EMI shielding effectiveness (SE) in the X-band frequency range. The study reveals that the morphology, influenced by the processing strategy, significantly impacts the EMI SE properties of the printed samples. Particularly, ternary hybrids 3/3/3 wt% (CNT/GNP/CB) nanocomposites demonstrate promising electrical (0.003 S/cm), electromagnetic (29 dB of total attenuation), and mechanical performance (elastic modulus of 3080 MPa), with a clear advantage observed in those processed via direct extrusion. These nanocomposites were validated as feedstock filaments for 3D printing, and the printed sample exceeds the injection molded behavior for the composition 3/3/3 wt% (CNT/GNP/CB), achieving 40 dB of total attenuation at 11.8 GHz. The findings contribute valuable knowledge into tailoring nanocomposite formulations for additive manufacturing applications in EMI shielding, providing a nuanced understanding of the interplay between processing strategies, nanocomposite morphology, and resulting material properties.

Towards Large-Area Black Phosphorous Midwave-IR Optoelectronics

High-efficiency mid-wavelength infrared (mid-IR, 3–5 µm) optoelectronics, including light emitting diodes (LEDs) and photodetectors, are of high demand for emerging applications in spectroscopy, imaging, and gas sensing. Black phosphorus (bP) has emerged as a unique optoelectronic material for mid-IR applications with performances surpassing those of conventional III-V and II-VI semiconductors of similar bandgap. In this talk, I will present recent advancements on understanding and controlling the radiative and non-radiative recombination rates as a function of bP thickness. We observe higher photoluminescence quantum yields in bP as compared to conventional III-V and II-VI semiconductors of similar bandgaps due to the smaller Auger recombination rate. As a result, bP mid-IR LEDs with external quantum efficiency of >4% are reported, outperforming the state-of-the-art in this wavelength range. Furthermore, device encapsulation and packaging technologies are explored with extrapolated half lifetime of ~15,000 hours based on accelerated lifetime measurements. Finally, I will present strategies for large-area device fabrication and processing.

Gdcov2sinex: a Python conversion tool from GipsyX’s gdcov file to SINEX file

The Solution INdependent EXchange (SINEX) file format, an international standard for the exchange of information, is essential in the Global Navigation Satellite System (GNSS) processing strategies that integrate different software applications. GNSS data processing using Jet Propulsion Laboratory’s (JPL) GipsyX software, which employs the Precise Point Positioning (PPP) technique, generates positions and covariance matrices in gdcov files, a GipsyX file format specific for storing this information. GipsyX software provides a tool, named mkDailySinex.py, to convert from gdcov file to SINEX file, but it is designed specifically for creating daily JPL SINEX files and does not function properly for non-JPL GipsyX users. I have developed a Python 3 program, named gdcov2sinex.py, which solves this problem, enabling any user to perform the SINEX conversion and, therefore, apply the processing strategies that integrate GipsyX with other software, such as GAMIT/GLOBK. The new python tool presented herein takes advantage of the capabilities of mkDailySinex.py and provides all its default options, but also expands the number of selectable options, which can be useful to the user. The source code, user manual, and a sample dataset of gdcov2sinex.py are provided as electronic supplementary material of this paper.

An enhanced Eco1 retron editor enables precision genome engineering in human cells from a single-copy integrated lentivirus.

Retrons are a retroelement class found in diverse prokaryotes that can be adapted to augment CRISPR-Cas9 genome engineering technology to efficiently rewrite short stretches of genetic information in bacteria and yeast; however, efficiency in human cells has been limited by unknown factors. We identified non-coding RNA (ncRNA) instability and impaired Cas9 activity as major contributors to poor retron editor efficiency. We re-engineered the Eco1 ncRNA to incorporate an exoribonuclease-resistant RNA pseudoknot from the Zika virus 3' UTR and devised an RNA processing strategy using Csy4 ribonuclease to liberate the sgRNA and ncRNA. These modifications yielded a ncRNA with 5'- and 3'-end protection and an sgRNA with minimal 5' extension. This strategy increased steady-state ncRNA levels and rescued Cas9 activity leading to enhanced efficiency of the Eco1 retron editor in human cells. The enhanced Eco1 retron editor enabled the insertion of missense mutations in human cells from a single integrated lentivirus, thereby ensuring genotype-phenotype linkage over multiple cell divisions. This work reveals a previously unappreciated role for ncRNA stability in retron editor efficiency in human cells. Here we present an enhanced Eco1 retron editor that enables efficient introduction of missense mutations in human cells from a single heritable genome copy.

On Unsupervised Multiclass Change Detection Using Dual-Polarimetric SAR Data

Change detection using SAR data has been an active topic in various applications. Because conventional change detection identifies signal changes in single-pol radar observations, they cannot separately detect different kinds of change associated with different ground parameters. In this study, we investigated the comprehensive use of dual-pol parameters and proposed a novel dual-pol-based change detection framework utilizing different dual-pol scatter-type indicators. To optimize the exploitation of dual-pol change information, we presented a two-step processing strategy that divides the multiclass change detection process into a binary detection step that identifies the presence of changes and the classification step that distinguishes the types of change. In the detection stage, each dual-pol parameter was considered as an independent information source. Assuming potential conflict between dual-pol parameters, a disjunctive combination of detection results from different dual-pol parameters was applied to obtain the final detection result. In the classification step, an unsupervised change classification strategy was proposed based on the change direction and magnitude of the dual-pol parameters within the change class. Experimental results exhibited significantly improved detectability across a wide change spectrum compared with previous dual-pol-based change detection approaches. They also demonstrated the possibility of distinguishing different semantic changes without in situ ground data.

Exploring chemical markers and identifying phenolic markers using a metabolomics strategy and chemometrics to study the different origins of defatted Coix seed

Coix seed, a prevalent medicinal and food-homologous plant, is extensively consumed in Asia. It has various pharmacological properties, such as anti-inflammatory and anticancer effects. Coix seed oil, as its main component, is widely produced. However, during the industrial production process of Coix seed oil, substantial byproducts are produced, namely, defatted Coix seeds, which are also worth researching. Currently, it remains unclear whether there will be differences in defatted Coix seeds obtained from different geographical locations, with previous studies reporting that phenolic compounds in defatted Coix seeds have a significant utilization value. In this study, firstly, the TPC and TFC of samples collected in three temperature zones were detected. Subsequently, UPLC-Q-TOF/MS was used to analyze the samples, and a metabolomics data processing strategy and chemometric analysis method were established. We have confirmed the presence of flavonoids and phenolic compounds in 30 batches of Coix seed from different temperature zones in China, and concluded that the overall quality of Coix seed from different batches is relatively stable. With the established strategy, 12 characteristic chemical markers were identified, and 5 valuable phenolic chemical markers were selected for distinguishing the origin of Coix seed and evaluating the quality of defatted Coix seed. Among them, proanthocyanidin A2 has the highest content in defatted Coix seed in subtropical regions, while the content of caffeic acid, naringin, rutin, and chlorogenic acid decreases from north to south. The strategy proposed in this study may provide some basis for the quality control and rational use of defatted Coix seeds.

Effects of deformation-induced BCC martensitic transformation on uniaxial ductility and biaxial stretchability in metastable ferrous medium-entropy alloys

This study investigated the effects of deformation-induced martensitic transformation (DIMT) on the stretchability and fracture behavior of thin sheets made from ferrous medium-entropy alloys (MEAs). Analyses of punch load-stroke curves from Erichsen tests revealed that DIMT occurred in the 60 at% Fe MEA, 64 at% Fe MEA, and 304 stainless steel (60Fe, 64Fe, and 304SS alloys, respectively). This DIMT affects necking onset, uniform elongation, and the Erichsen index (EI) under diverse stress conditions. DIMT plays a significant role in strain hardening and mechanical instability during uniaxial tests, enhancing strain hardening through martensite formation and also initiating necking due to non-uniform deformation. Particularly in 64Fe, DIMT-induced early necking resulted in reduced uniform elongation. On the other hand, biaxial Erichsen test results demonstrated further strain hardening due to martensite-induced strain accommodation, leading to enhanced resistance to deformation during biaxial loading. The distinctive shapes of punch load-stroke curves in biaxial tests, compared to stress-strain curves from uniaxial tensile tests, are a result of the interaction between DIMT and stretching directions, as well as its alloy-dependent phase stability. In 60Fe, transformed martensite contributed to strain hardening and delayed necking initiation, while extensive martensite transformation in 64Fe enhanced strain hardening further. This understanding of the complex correlation between DIMT, strain hardening, and mechanical instability holds potential for alloy optimization and developing processing strategies, applicable to automotive manufacturing and structural engineering industries.

Operando study of the dynamic evolution of multiple Fe-rich intermetallics of an Al recycled alloy in solidification by synchrotron X-ray and machine learning

Using synchrotron X-ray diffraction, tomography and machine-learning enabled phase segmentation strategy, we have studied under operando conditions the nucleation, co-growth and dynamic interplays among the dendritic and multiple intermetallic phases of a typical recycled Al alloy (Al5Cu1.5Fe1Si, wt.%) in solidification with and without ultrasound. The research has revealed and elucidated the underlying mechanisms that drive the formation of the very complex and convoluted Fe-rich phases with rhombic dodecahedron and 3D skeleton networks (the so-called Chinese-script type morphology). Through statistical microstructural analyses and numerical modelling of the ultrasound melt processing, the research has demonstrated that a short period of ultrasound processing of just 7 s in the liquid state is able to reduce the average size of the α-Al dendrites and the Fe-containing intermetallic phases by ∼5 times compared to the cases without ultrasound. For the first time, this work has revealed fully the nucleation and growth dynamics of the convoluted morphology of the Fe-containing intermetallic phases in 4D domain. The research has also demonstrated clearly the beneficial effects of applying ultrasound to control the Fe phases' morphology in recycled Al alloys and it is one of the most effective and green processing strategies.

Combining Ultrafast Laser Texturing and Laser Hardening to Enhance Surface Durability by Improving Hardness and Wear Performance

Aluminum alloy 7075 is utilized widely across marine, aerospace, and automotive sectors. However, its surface wear resistance has hindered its application in certain tribological environments. Addressing this challenge, the current study examines a hybrid laser method to increase surface wear resistance by combining two techniques: ultrafast laser texturing and laser‐based surface hardening. Ultrafast laser processing is conducted using 3 W laser power, 100 kHz pulse repetition rate, 4 mm s−1 scanning speed, and three different scan patterns. After the texturing operation, laser‐based surface hardening is then performed on these textures using a continuous wave laser. The laser heat treatment is conducted using laser powers of 400 and 500 W with three different scan speeds of 1, 2, and 3 mm s−1. Microhardness evaluations show a notable increase in hardness, with the hardest sample exhibiting a 17.8% increase compared to the pristine sample. The laser‐textured and laser heat‐treated samples exhibit a significant reduction in the average coefficient of friction and wear volumes compared to samples that were laser‐textured but not laser heat‐treated. The investigated laser processing strategy offers a promising approach for surface modification, enhancing both mechanical properties and wear resistance of aluminum alloy 7075 surfaces.

Influence of scanning strategies on microstructure and mechanical properties of laser powder bed fusion 2195 Al-Li alloy

Optimizing the scanning strategy is considered as an effective approach to address the challenges of laser powder bed fusion(LPBF). However, the impact of the scanning strategy on lightweight Al-Li alloys remains unclear. In this study, various scanning strategies LPBF were employed to process 2195 Al-Li alloy specimens, and a systematic investigation was conducted on the effects of the laser path on the defects, microstructure, and mechanical properties of 2195 Al-Li alloy. The results revealed that the specimens processed with the linear strategy exhibited the lowest gas pores and crack density, achieving a densification rate of 98.6 %. Furthermore, adjusting the scanning strategy led to changes in grain morphology among specimens; those processed with linear scanning demonstrated smaller average grain size (19.7 μm), a lower percentage of high-angle grain boundaries (HAGBs) (57.4 %), and a higher texture index compared to other strategies tested. When employing linear strategy for processing, tensile performance reached 315.03 MPa with 5.03 % elongation due to concise thermal history and fast cooling conditions associated with this particular scanning method. This study provides valuable insights into optimizing scanning strategies for LPBF 2195 Al-Li alloy.

Techno-economic assessment of industrial food waste composting facility: Evaluating bulking agents, processing strategies, and market dynamics

Techno-economic assessment (TEA) of a valorization of bulking agent (BA) ratios on the food waste compost value chain is made to assess economic feasibility. TEA was performed with two plans (Plan A: existing composting facilities; Plan B: new composting facilities) and each plan was under four scenarios. The BA (i.e. corn stalks, garden waste, and watermelon seedlings) ratio of 5 % (S1), 10 % (S2), 20 % (S3), and garden waste with a ratio of 20 % (S4). Results indicate that S2, with a net present value (NPV) of 128.9 million, represents Plan A’s most economically viable scenario. Although the total operating costs of S4 were 18.9 %–23.5 % higher, 25.6 %–42.2 % higher total revenue made S4 have an NPV of 92.9 million, making it the most viable scenario in Plan B. All scenarios show positive NPV within a ± 20 % fluctuation range. Organic fertilizer price, government subsidies, and processing capacity were the key factors influencing NPV.

Beyond head and wings: Unveiling influence of diet, body size, and phylogeny on the evolution of the femur in phyllostomid bats.

Phyllostomidae, the most diverse family of Neotropical bats, encompass 230 species with varied dietary habits and food acquisition methods. Their feeding niche diversification has shaped skull and wing morphologies through natural selection, reflecting food processing and flight strategies. Yet, evolution of bat hindlimbs, especially in phyllostomids, remains little understood. Previous studies highlighted the femur's morphology as a key to understanding the evolution of quadrupedalism in yangochiropteran bats, including the adept walking observed in vampire bats (Desmodontinae). Here, we aimed to describe the femoral morphological variation in Phyllostomidae, correlating this with body size and assessing the effects of phylogenetic history, dietary habits, and hindlimb usage. Analyzing 15 femoral traits from 45 species across 9 subfamilies through phylogenetically informed methods, we discovered a significant phylogenetic structure in femoral morphology. Allometric analysis indicated that body mass accounts for about 85% of the variance in phyllostomid femoral size and about 11% in femoral shape. Relatively smaller femurs showed to be typical in Stenodermatinae, Lonchophyllinae, and Glossophaginae, in contrast to the larger femurs of Phyllostominae, Desmodontinae, Micronycterinae, and Lonchorrhininae. Furthermore, extensive femur shape variation was detected, with the most distinct morphologies in vampire bats, followed by frugivorous species. Adaptive evolutionary models related to diet more effectively explained variations in femoral relative size and shape than stochastic models. Contrary to the conventional belief of limited functional demand on bat femurs, our findings suggest that femoral morphology is significantly influenced by functional demands associated with diet and food capture, in addition to being partially structured by body size and shared evolutionary history.

Synthesis and Processing Strategy for High-Bandgap PbS Quantum Dots: A Promising Candidate for Harvesting High-Energy Photons in Solar Cells.

The wide tunability of the energy bandgap of colloidal lead sulfide (PbS) quantum dots (QDs) has uniquely positioned them for the development of single junction and tandem solar cells. While there have been substantial advancements in moderate and narrow bandgap PbS QDs-ideal for single junction solar cells and the bottom cell in tandem solar cells, respectively; progress has been limited in high-bandgap PbS QDs that are ideally suited for the formation of the top cell in tandem solar cells. The development of appropriate high bandgap PbS QDs would be a major advancement toward realizing efficient all-QD tandem solar cells utilizing different sizes of PbS QDs. Here, we report a comprehensive approach encompassing synthetic strategy, ligand engineering, and hole transport layer (HTL) modification to implement high-bandgap PbS QDs into solar cell devices. We achieved a greater degree of size homogeneity in high-bandgap PbS QDs through the use of a growth retarding agent and a partial passivation strategy. By adjusting the ligand polarity, we successfully grow HTL over the QD film to fabricate solar cells. With the aid of an interface modifying layer, we incorporated an organic HTL for the realization of high-performance solar cells. These solar cells exhibited an impressive open-circuit voltage of 0.824 V and a power conversion efficiency of 10.7%, marking a 360% improvement over previous results.

Expanding the neighbourhood watch: Orthographic neighbours in isiXhosa reading and spelling

Background: Lexical properties such as orthographic neighbours have been shown to have an influence on reading and writing; however, this phenomenon is yet to be explored in the Southern Bantu languages.Objectives: We investigate the role of orthographic neighbourhood density and neighbourhood frequency in reading and spelling in Grade 3 isiXhosa home-language learners. The aim is to establish whether orthographic neighbours facilitate or hinder reading and spelling. The dual-route model of orthographic processing is used to interpret our findings.Method: Data were collected from 97 Grade 3 isiXhosa home-language learners. Lexical decision, word reading, and spelling tasks were administered. Tasks included both real and pseudowords varying in orthographic neighbourhood density and neighbourhood frequency, while controlling for word length and word frequency.Results: Findings show a significant inhibitory effect of orthographic neighbourhood frequency and word length for spelling accuracy. Longer words with higher frequency neighbours were more likely to be spelt incorrectly. No effect was recorded for the lexical decision and word reading tasks.Conclusion: Our findings suggest that spelling in isiXhosa relies partially on lexical processing strategies, which allows for the inhibitory effects of orthographic neighbours to be observed. While in reading, sublexical processing is more prominent, therefore mitigating the effect of orthographic neighbours.Contribution: Empirical evidence for the effects of orthographic neighbours, specifically in a Southern Bantu language, provides a clearer picture of the underlying cognitive-linguistic processes involved in reading and writing. This evidence may contribute to the development of targeted pedagogical practices to address spelling errors.

The investigation of traffic flow prediction and optimization based on Spark

Spark and flink have made great strides in big data processing in recent years. Although the current spark can process a large amount of data at the same time, it still has a lot of shortcomings in the transparency, credibility, and practicality of the model. This paper provides a comprehensive overview of how to tackle the performance bottlenecks, insufficient model interpretability, and lack of regional adaptability faced by Spark and Flink in big data processing. It discusses the introduction of interpretable algorithms such as SHAP and LIME to enhance the transparency and user trust of neural network models. Then it discusses how to combine time-aware transfer learning and Geographic Information Systems (GIS) technology to enhance the technology generalization and adaptability of Spark machine learning models. Time-aware transfer learning uses historical datas temporal evolution to ensure models perform well in new time periods or scenarios, while GIS technology enables more precise predictions and analyses based on geographical data, enhancing spatial adaptability. Lastly, the study explores hybrid processing strategies by integrating Apache Flink, Kafka Streams, and Spark batch processing frameworks. This approach not only facilitates efficient real-time data processing and detailed analysis but also enhances the models flexibility and processing capabilities in complex data scenarios. By integrating these techniques, it is possible to improve the efficiency and effectiveness of big data processing frameworks in addressing complex real-world challenges, thereby advancing technology and application development in related fields.

Mismatch negativity between discriminating and undiscriminating participants on the front-back sound localization

Sound localization in the front-back dimension is reported to be challenging, with individual differences. We investigated whether auditory discrimination processing in the brain differs based on front-back sound localization ability. This study conducted an auditory oddball task using speakers in front of and behind the participants. We used event-related brain potentials to examine the deviance detection process between groups that could and could not discriminate front-back sound localization. The results indicated that mismatch negativity (MMN) occurred during the deviance detection process, and P2 amplitude differed between standard and deviant locations in both groups. However, the latency of MMN was shorter in the group that could discriminate front-back sounds than in the group that could not. Additionally, N1 amplitude increased for deviant locations compared to standard ones only in the discriminating group. In conclusion, the sensory memories matching process based on traces of previously presented stimuli (MMN, P2) occurred regardless of discrimination ability. However, the response to changes in the physical properties of sounds (MMN latency, N1 amplitude) differed depending on the ability to discriminate front-back sounds. Our findings suggest that the brain may have different processing strategies for the two directions even without subjective recognition of the front-back direction of incoming sounds.

Application of Machine Learning Techniques for Fake News Classification

ABSTRACT Fake News consists of disseminating fake news in various social and digital media such as newspapers, television networks, and the internet. Fake news is not a new phenomenon in human behavior. However, the current dissemination is very different from what happened in the past. Social networks and the contemporary world have made it possible for the spread of lies to occur quickly and even intentionally. This causes serious problems, and its impacts can be felt in the real world. The identification of fake news can be useful in several contexts and can be used, for example, as a news filter in the virtual space. Thus, the present work aims to propose and evaluate strategies for processing and applying machine learning models, to improve the performance of classifiers in the problem of identifying fake news in Brazilian news.

Calving front monitoring at a subseasonal resolution: a deep learning application for Greenland glaciers

Abstract. The mass balance of the Greenland Ice Sheet is strongly influenced by the dynamics of its outlet glaciers. Therefore, it is of paramount importance to accurately and continuously monitor these glaciers, especially the variation in their frontal positions. A temporally comprehensive parameterization of glacier calving is essential for understanding dynamic changes and constraining ice sheet modeling. However, many current calving front records are limited in terms of temporal resolution as they rely on manual delineation, which is laborious and not appropriate considering the increasing amount of satellite imagery available. In this contribution, we address this problem by applying an automated method to extract calving fronts from optical satellite imagery. The core of this workflow builds on recent advances in the field of deep learning while taking full advantage of multispectral input information. The performance of the method is evaluated using three independent test datasets. For the three datasets, we calculate mean delineation errors of 61.2, 73.7, and 73.5 m, respectively. Eventually, we apply the technique to Landsat-8 imagery. We generate 9243 calving front positions across 23 outlet glaciers in Greenland for the period 2013–2021. Resulting time series not only resolve long-term and seasonal signals but also resolve subseasonal patterns. We discuss the implications for glaciological studies and present a first application for analyzing the effect of bedrock topography on calving front variations. Our method and derived results represent an important step towards the development of intelligent processing strategies for glacier monitoring, opening up new possibilities for studying and modeling the dynamics of Greenland's outlet glaciers.

ProteinFlow: An advanced framework for feature engineering in protein data analysis.

In the burgeoning field of proteins, the effective analysis of intricate protein data remains a formidable challenge, necessitating advanced computational tools for data processing, feature extraction, and interpretation. This study introduces ProteinFlow, an innovative framework designed to revolutionize feature engineering in protein data analysis. ProteinFlow stands out by offering enhanced efficiency in data collection and preprocessing, along with advanced capabilities in feature extraction, directly addressing the complexities inherent in multidimensional protein data sets. Through a comparative analysis, ProteinFlow demonstrated a significant improvement over traditional methods, notably reducing data preprocessing time and expanding the scope of biologically significant features identified. The framework's parallel data processing strategy and advanced algorithms ensure not only rapid data handling but also the extraction of comprehensive, meaningful insights from protein sequences, structures, and interactions. Furthermore, ProteinFlow exhibits remarkable scalability, adeptly managing large-scale data sets without compromising performance, a crucial attribute in the era of big data.

Unveiling the influence of high hydrostatic pressure and protein interactions on the color and chemical stability of cyanidin-3-O-glucoside

This study explored how high hydrostatic pressure (HHP) and proteins (i.e., BSA and HSA) influence the color and chemical stability of cyanidin-3-O-glucoside (C3G) at neutral pH. HHP treatments (100–500 MPa, 0–20 min, 25 °C) did not affect C3G content in phosphate buffer (PB) and MOPS buffer. However, significant color loss of C3G occurred in PB due to pressure-induced pH reduction (e.g., from 7 to 4.8 at 500 MPa), which accelerated the hydration of C3G, converting it from colored to colorless species. Consequently, MOPS buffer was employed for subsequent stability experiments to assess the impact of protein and HHP on the thermal, storage, and UV light stability of C3G. Initially, rapid color loss occurred during heating and storage, primarily due to the reversible hydration of C3G until equilibrium with colorless species was reached, followed by slower parallel degradation. HSA increased the fraction of colored species at equilibrium but accelerated thermal degradation, while BSA had minimal effects. UV light irradiation accelerated the degradation of C3G colored species, causing direct degradation without conversion to colorless species, a process further intensified by the presence of proteins. HHP exhibited a negligible effect on C3G stability regardless of protein addition. These findings provide insights into anthocyanin stability under HHP and protein interactions, contributing to the development of future formulation and processing strategies for improved stability and broader applications.