Betaine Solutions′ Interfacial Dilational Rheology: Anionic Surfactant and Polymer Effects

Zamari Niazi^*
*Correspondence: Zamari Niazi, Department of Pharmacy, University of Illinois, Chicago, IL 61820, USA, Email:

Keywords

CNNs • Machine learning • Reinforcement learning • Healthcare

Introduction

Transfer learning has gained prominence in recent years as an effective technique for leveraging knowledge from pretrained models. By using a model pretrained on a large dataset, transfer learning allows for faster and more accurate training on a specific task with limited labeled data. The availability of pretrained models, such as BERT (Bidirectional Encoder Representations from Transformers) for natural language processing and ImageNet for computer vision, has facilitated breakthroughs in various domains. Transfer learning has become a key strategy for practical machine learning applications, particularly in scenarios where data is scarce. Reinforcement Learning (RL) has shown great potential in training autonomous agents to make intelligent decisions in dynamic environments. Recent developments in RL have focused on improving sample efficiency and stability of learning algorithms. Techniques like Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO) and Soft Actor-Critic (SAC) have yielded impressive results in tasks such as robotics, game playing, and autonomous driving. RL has the potential to revolutionize fields such as healthcare, finance, and logistics by enabling autonomous decision-making systems. Generative models, particularly Generative Adversarial Networks (GANs) and Variational Auto Encoders (VAEs), have made significant strides in unsupervised learning. GANs can generate realistic samples in various domains, including images, music, and text, while VAEs enable latent space representations and data generation. Recent advancements in GANs have addressed challenges like mode collapse, instability, and training dynamics, leading to more stable and highquality results. Unsupervised learning techniques have the potential to unlock valuable insights from unannotated data, facilitating better understanding and utilization of complex datasets [1].

Literature Review

Interfacial dilational rheology is a crucial field of study that investigates the mechanical properties of liquid interfaces, particularly how they respond to deformation and stress. Understanding interfacial rheology is vital in various applications, ranging from food and cosmetic formulations to enhanced oil recovery and environmental remediation. Betaine solutions have gained significant attention in recent years due to their unique properties in stabilizing interfaces. In this article, we delve into the effects of anionic surfactants and polymers on the interfacial dilational rheology of betaine solutions.

Interfacial rheology deals with the study of how the mechanical properties of an interface, such as a liquid-gas or liquid-liquid interface, respond to deformation and stress. Dilational rheology, specifically, explores the interface's ability to withstand stretching or compression, often measured by the interfacial tension. This parameter is crucial in determining the stability of dispersed systems, emulsions, foams, and other colloidal systems. Betaine solutions offer unique and valuable interfacial properties due to their zwitterionic nature. When combined with anionic surfactants and polymers, their interfacial dilational rheology is significantly influenced. These mixtures have found applications in various industries, including personal care, household products, and industrial processes.

Understanding the effects of anionic surfactants and polymers on betaine solutions' interfacial dilational rheology is crucial for tailoring formulations to meet specific stability and performance requirements. Continued research in this area will undoubtedly lead to further innovations and improvements in colloidal and interfacial science, benefiting a wide range of industries and applications.

Discussion

As machine learning models are increasingly being deployed in critical domains, the need for transparency and interpretability has become crucial. Recent developments have focused on creating models that can provide explanations for their predictions, enabling humans to understand and trust the decision-making process. Techniques like attention mechanisms, saliency maps, and gradient-based attribution methods have been developed to highlight the important features and factors influencing the model's output [2]. Explainable AI plays a pivotal role in domains such as healthcare, finance, and law, where model decisions must be justified and comprehensible. With the growing concerns surrounding data privacy and security, federated learning has emerged as a promising approach. Federated learning allows training models across decentralized devices without the need to share sensitive data. Recent advancements in federated learning have focused on improving communication efficiency, reducing privacy risks, and addressing heterogeneity among devices. Techniques such as secure aggregation, differential privacy, and homomorphic encryption have been employed to protect user privacy while ensuring the robustness and accuracy of models trained in federated settings.

Machine learning has undergone significant advancements in recent years, with breakthroughs in deep learning, transfer learning, reinforcement learning, generative models, explainable AI, and privacy-preserving techniques [3]. These developments have not only enhanced the performance and capabilities of machine learning models but have also enabled their deployment in critical domains such as healthcare, finance, and autonomous systems. As machine learning continues to evolve, it holds immense potential for solving complex problems and driving innovation across various industries. Understanding and embracing these recent developments will be essential for organizations and researchers seeking to leverage the power of machine learning based technologies. Machine learning has revolutionized numerous industries, ranging from healthcare to finance, and it continues to evolve rapidly. Over the past few years, several significant developments have taken place in the field of machine learning, expanding its capabilities and applicability. In this article, we will provide a comprehensive review of some of the recent advancements in machine learning-based techniques, exploring their potential impact and implications [4].

Deep learning, a subset of machine learning, has gained considerable attention due to its ability to learn complex patterns and representations from vast amounts of data. Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), and Generative Adversarial Networks (GANs) are some of the prominent architectures within deep learning. Recent advancements in deep learning have led to significant breakthroughs in various domains [5]. For example, in computer vision, CNNs have achieved exceptional performance in image classification, object detection, and segmentation tasks. RNNs have shown great promise in Natural Language Processing (NLP), enabling tasks such as sentiment analysis, machine translation and text generation. GANs have revolutionized image synthesis, enabling the generation of realistic images and videos.

Transfer learning has emerged as a powerful technique in machine learning, allowing models trained on one task to be adapted for another related task. This approach leverages knowledge gained from a large labeled dataset and transfers it to a different but related problem with limited labeled data [6]. In summary, betaine solutions' interfacial dilational rheology, in combination with anionic surfactants and polymers, is a promising area of study that holds immense potential for the development of advanced formulations with superior stability and performance characteristics.

Conclusion

Machine learning has undergone rapid advancements in recent years, driven by breakthroughs in deep learning, transfer learning, reinforcement learning, explainable AI, and privacy-preserving techniques. These developments have expanded the capabilities of machine learning models, making them more powerful, interpretable and privacy-conscious. Pretrained models, such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pretrained Transformer), have demonstrated remarkable performance in various NLP tasks. By training on large-scale corpora, these models acquire a broad understanding of language, enabling them to perform well on a wide range of downstream tasks. Transfer learning and pretrained models have significantly reduced the need for extensive labeled data, making machine learning more accessible and practical for many applications. Reinforcement Learning (RL) involves training an agent to make sequential decisions by interacting with an environment. Recent developments in RL have led to significant advancements in the field of autonomous systems. One notable achievement is the success of RL in playing complex games, such as Go, chess, and video games. AlphaGo, developed by DeepMind, defeated world champion Go players, showcasing the potential of RL in tackling complex decision-making problems.

Acknowledgement

None.

Conflict of Interest

None.

References

1. Patino, Juan M. Rodríguez, Marta Cejudo Fernández, Cecilio Carrera Sánchez and Ma Rosario Rodríguez Niño. "Structural and shear characteristics of adsorbed sodium caseinate and monoglyceride mixed monolayers at the air–water interface."J Colloid Interface Sci313 (2007): 141-151.

Google Scholar, Crossref, Indexed at

2. Javadi, Aliyar, Saeid Dowlati, Sara Shourni and Reinhard Miller, et al. "Experimental techniques to study protein–surfactant interactions: New insights into competitive adsorptions via drop subphase and interface exchange."Adv Colloid Interface Sci 301 (2022): 102601.

Google Scholar, Crossref, Indexed at

3. Fainerman, V. B, J. T. Petkov and R. Miller. "Surface dilational viscoelasticity of C14EO8 micellar solution studied by bubble profile analysis tensiometry."Langmuir 24 (2008): 6447-6452.

Google Scholar, Crossref, Indexed at

4. Huang, Yu-Ping, Lei Zhang, Lu Zhang and Lan Luo, et al. "Dynamic interfacial dilational properties of hydroxy-substituted alkyl benzenesulfonates."J Phys Chem B111 (2007): 5640-5647.

Google Scholar, Crossref, Indexed at

5. Zhang, Lei, Xiao-Chun Wang, Qing-Tao Gong and Lu Zhang, et al. "Interfacial dilational properties of tri-substituted alkyl benzene sulfonates at air/water and decane/water interfaces."J Colloid Interface Sci 327 (2008): 451-458.

Google Scholar, Crossref, Indexed at

6. Noskov, B. A., A. V. Akentiev, A. Yu Bilibin and I. M. Zorin, et al. "Dilational surface viscoelasticity of polymer solutions."Adv Colloid Interface Sci 104 (2003): 245-271.

Google Scholar, Crossref, Indexed at