Artificial intelligence application for rapid fabrication of size-tunable PLGA microparticles in microfluidics

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

Simple microfluidic devices for in situ detection of water contamination: a state-of-art review

Water security is an important global issue that is pivotal in the pursuit of sustainable resources for future generations. It is a multifaceted concept that combines water availability with the quality of the water's chemical, biological, and physical characteristics to ensure its suitability and safety. Water quality is a focal aspect of water security. Quality index data are determined and provided via laboratory testing using expensive instrumentation with high maintenance costs and expertise. Due to increased practices in this sector that can compromise water quality, innovative technologies such as microfluidics are necessary to accelerate the timeline of test procedures. Microfluidic technology demonstrates sophisticated functionality in various applications due to the chip's miniaturization system that can control the movement of fluids in tiny amounts and be used for onsite testing when integrated with smart applications. This review aims to highlight the basics of microfluidic technology starting from the component system to the properties of the chip's fabricated materials. The published research on developing microfluidic sensor devices for monitoring chemical and biological contaminants in water is summarized to understand the obstacles and challenges and explore future opportunities for advancement in water quality monitoring.

Machine learning assisted exploration of the influential parameters on the PLGA nanoparticles

Poly (lactic-co-glycolic acid) (PLGA)-based nanoparticles (NPs) are widely investigated as drug delivery systems. However, despite the numerous reviews and research papers discussing various physicochemical and technical properties that affect NP size and drug loading characteristics, predicting the influential features remains difficult. In the present study, we employed four different machine learning (ML) techniques to create ML models using effective parameters related to NP size, encapsulation efficiency (E.E.%), and drug loading (D.L.%). These parameters were extracted from the different literature. Least Absolute Shrinkage and Selection Operator was used to investigate the input parameters and identify the most influential features (descriptors). Initially, ML models were trained and validated using tenfold validation methods, and subsequently, next their performances were evaluated and compared in terms of absolute error, mean absolute, error and R-square. After comparing the performance of different ML models, we decided to use support vector regression for predicting the size and E.E.% and random forest for predicting the D.L.% of PLGA-based NPs. Furthermore, we investigated the interactions between these target variables using ML methods and found that size and E.E.% are interrelated, while D.L.% shows no significant relationship with the other targets. Among these variables, E.E.% was identified as the most influential parameter affecting the NPs' size. Additionally, we found that certain physicochemical properties of PLGA, including molecular weight (Mw) and the lactide-to-glycolide (LA/GA) ratio, are the most determining features for E.E.% and D.L.% of the final NPs, respectively.

Research Progress on Immunomodulatory Effects of Poly (Lactic-co- Glycolic Acid) Nanoparticles Loaded with Traditional Chinese Medicine Monomers

Immunomodulatory mechanisms are indispensable and key factors in maintaining the balance of the environment in humans. When the immune function of the immune system is impaired, autoimmune diseases occur. Excessive body fatigue, natural aging of the human body, malnutrition, genetic factors and other reasons cause low immune function, due to which the body is prone to being infected by bacteria or cancer. Clinically, the existing therapeutic drugs still have problems such as high toxicity, long treatment cycle, drug resistance and high price, so we still need to explore and develop a high efficiency and low toxicity drug. Poly(lactic-co-glycolic acid) (PLGA) refers to a nontoxic polymer compound that exhibits excellent biocompatibility. Traditional Chinese medicine (TCM) monomers come from natural plants, and have the characteristics of high efficiency and low toxicity. Applying PLGA to TCM monomers can make up for the defects of traditional dosage forms, improve bioavailability, reduce the frequency and dosage of drug use, and reduce toxicity and side effects, thus having the characteristics of sustained release and targeting. Accordingly, PLGA nanoparticles loaded with TCM monomers have been the focus of development. The previous research on drug loading advantages, preparation methods, and immune regulation of TCM PLGA nanoparticles is summarized in the following sections.

Artificial neural network modelling hydrodenticity for optimal design by microfluidics of polymer nanoparticles to apply in magnetic resonance imaging

The engineering of nanoparticles impacts the control of their nano-bio interactions at each level of the delivery pathway. Therefore, optimal nanoparticle physicochemical properties should be identified to favour on-target interactions and deliver efficiently active compounds to a specific target. To date, traditional batch processes do not guarantee the reproducibility of results and low polydispersity index of the nanostructures, while microfluidics has emerged as cost effectiveness, short-production time approach to control the nanoparticle size and size distribution. Several thermodynamic processes have been implemented in microfluidics, such as nanoprecipitation, ionotropic gelation, self-assembly, etc., to produce nanoparticles in a continuous mode and high throughput way.   In this work, we show how the Artificial Neural Network (ANN) can be adopted to model the impact of microfluidic parameters (namely, flow rates and polymer concentrations) on the size of the nanoparticles. Promising results have been obtained, with the highest model accuracy reaching 98.9 %, thus confirming the proposed approach's potential applicability for an ANN-guided biopolymer nanoparticle design for biomedical applications. Nanostructures with different degrees of complexity are analysed, and a proof-of-concept machine learning approach is proposed to evaluate Hydrodenticity in biopolymer matrices. STATEMENT OF SIGNIFICANCE: Size, shape and surface charge determine nano-bio interactions of nanoparticles and their ability to target diseases. The ideal nanoparticle design avoids off-target interactions and favours on-target interactions. So, tools enabling the identification of the optimal nanoparticle physicochemical properties for delivery to a specific target are required. In this work, we evaluate the use of Artificial Neural Network (ANN) to analyse the role of microfluidic parameters in predicting the optimal size of the different hydrogel nanoparticles and their ability to trigger Hydrodenticity.

Generation of Photopolymerized Microparticles Based on PEGDA Hydrogel Using T-Junction Microfluidic Devices: Effect of the Flow Rates

The formation of microparticles (MPs) of biocompatible and biodegradable hydrogels such as polyethylene glycol diacrylate (PEGDA) utilizing microfluidic devices is an attractive option for entrapment and encapsulation of active principles and microorganisms. Our research group has presented in previous studies a formulation to produce these hydrogels with adequate physical and mechanical characteristics for their use in the formation of MPs. In this work, hydrogel MPs are formed based on PEGDA using a microfluidic device with a T-junction design, and the MPs become hydrogel through a system of photopolymerization. The diameters of the MPs are evaluated as a function of the hydrodynamic condition flow rates of the continuous (Qc) and disperse (Qd) phases, measured by optical microscopy, and characterized through scanning electron microscopy. As a result, the following behavior is found: the diameter is inversely proportional to the increase in flow in the continuous phase (Qc), and it has a significant statistical effect that is greater than that in the flow of the disperse phase (Qd). While the diameter of the MPs is proportional to Qd, it does not have a significant statistical effect on the intervals of flow studied. Additionally, the MPs' polydispersity index (PDI) was measured for each experimental hydrodynamic condition, and all values were smaller than 0.05, indicating high homogeneity in the MPs. The microparticles have the potential to entrap pharmaceuticals and microorganisms, with possible pharmacological and bioremediation applications.

Chemo-radiotherapy with 177Lu-PLGA(RGF)-CXCR4L for the targeted treatment of colorectal cancer

Introduction

More than 1.9 million new cases of colorectal cancer and 935,000 deaths were estimated to have occurred worldwide in 2020. Therapies for metastatic colorectal cancer include cytotoxic chemotherapy and targeted therapies in multiple lines of treatment. Nevertheless, the optimal use of these agents has not yet been resolved. Regorafenib (RGF) is an Food and Drug Administration (FDA)-authorized multikinase inhibitor indicated for patients with metastatic colorectal cancer, non-responding to priority lines of chemotherapy and immunotherapy. Nanoparticles have been used in specific applications, such as site-specific drug delivery systems, cancer therapy, and clinical bioanalytical diagnostics. C-X-C Chemokine receptor type 4 (CXCR4) is the most widely-expressed chemokine receptor in more than 23 human cancer types, including colorectal cancer. This research aimed to synthesize and preclinically evaluate a targeted nanosystem for colorectal cancer chemo-radiotherapy using RGF encapsulated in Poly(D,L-lactic-co-glycolic acid) (PLGA) nanoparticles coated with a CXCR4 ligand (CXCR4L) and 177Lu as a therapeutic β-emitter.

Methods

Empty PLGA and PLGA(RGF) nanoparticles were prepared using the microfluidic method, followed by the DOTA and CXCR4L functionalization and nanoparticle radiolabeling with 177Lu. The final nanosystem gave a particle size of 280 nm with a polydispersity index of 0.347. In vitro and in vivo toxicity effects were assessed using the HCT116 colorectal cancer cell line.

Results

177Lu-PLGA(RGF)-CXCR4L nanoparticles decreased cell viability and proliferation by inhibiting Erk and Akt phosphorylation and promoting apoptosis. Moreover, in vivo administration of 177Lu-PLGA(RGF)-CXCR4L significantly reduced tumor growth in an HCT116 colorectal cancer xenograft model. The biokinetic profile showed hepatic and renal elimination.

Discussion

Data obtained in this research justify additional preclinical safety trials and the clinical evaluation of 177Lu-PLGA(RGF)-CXCR4L as a potential combined treatment of colorectal cancer.

Comparative study of lipid nanoparticle-based mRNA vaccine bioprocess with machine learning and combinatorial artificial neural network-design of experiment approach

To develop a combinatorial artificial-neural-network design-of-experiment (ANN-DOE) model, the effect of ionizable lipid, an ionizable lipid-to-cholesterol ratio, N/P ratio, flow rate ratio (FRR), and total flow rate (TFR) on the outcome responses of mRNA-LNP vaccine were evaluated using a definitive screening design (DSD) and machine learning (ML) algorithms. Particle size (PS), PDI, zeta potential (ZP), and encapsulation efficiency (EE) of mRNA-LNP were optimized within a defined constraint (PS 40-100 nm, PDI≤0.30, ZP≥(±)0.30 mV, EE≥70%), fed to ML algorithms (XGBoost, bootstrap forest, support vector machines, k-nearest neighbors, generalized regression-Lasso, ANN) and prediction was compared to ANN-DOE model. Increased FRR decreased the PS and increased ZP, while increased TFR increased PDI and ZP. Similarly, DOTAP and DOTMA produced higher ZP and EE. Particularly, a cationic ionizable lipid with an N/P ratio ≥6 provided a higher EE. ANN showed better predictive ability (R²=0.7269-0.9946), while XGBoost demonstrated better RASE (0.2833-2.9817). The ANN-DOE model outperformed both optimized ML models by R²=1.21% and RASE=43.51% (PS prediction), R²=0.23% and RASE=3.47% (PDI prediction), R²=5.73% and RASE=27.95% (ZP prediction), and R²=0.87% and RASE=36.95% (EE prediction), respectively, which demonstrated that ANN-DOE model was superior in predicting the bioprocess compared to independent models.

Microsystem Advances through Integration with Artificial Intelligence

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

Biomimetic natural biomaterials for tissue engineering and regenerative medicine: new biosynthesis methods, recent advances, and emerging applications

Biomimetic materials have emerged as attractive and competitive alternatives for tissue engineering (TE) and regenerative medicine. In contrast to conventional biomaterials or synthetic materials, biomimetic scaffolds based on natural biomaterial can offer cells a broad spectrum of biochemical and biophysical cues that mimic the in vivo extracellular matrix (ECM). Additionally, such materials have mechanical adaptability, microstructure interconnectivity, and inherent bioactivity, making them ideal for the design of living implants for specific applications in TE and regenerative medicine. This paper provides an overview for recent progress of biomimetic natural biomaterials (BNBMs), including advances in their preparation, functionality, potential applications and future challenges. We highlight recent advances in the fabrication of BNBMs and outline general strategies for functionalizing and tailoring the BNBMs with various biological and physicochemical characteristics of native ECM. Moreover, we offer an overview of recent key advances in the functionalization and applications of versatile BNBMs for TE applications. Finally, we conclude by offering our perspective on open challenges and future developments in this rapidly-evolving field.

In situ Formation of Polymer Microparticles in Bacterial Nanocellulose Using Alternative and Sustainable Solvents to Incorporate Lipophilic Drugs

Bacterial nanocellulose has been widely investigated in drug delivery, but the incorporation of lipophilic drugs and controlling release kinetics still remain a challenge. The inclusion of polymer particles to encapsulate drugs could address both problems but is reported sparely. In the present study, a formulation approach based on in situ precipitation of poly(lactic-co-glycolic acid) within bacterial nanocellulose was developed using and comparing the conventional solvent N-methyl-2-pyrrolidone and the alternative solvents poly(ethylene glycol), Cyrene^TM and ethyl lactate. Using the best-performing solvents N-methyl-2-pyrrolidone and ethyl lactate, their fast diffusion during phase inversion led to the formation of homogenously distributed polymer microparticles with average diameters between 2.0 and 6.6 µm within the cellulose matrix. Despite polymer inclusion, the water absorption value of the material still remained at ~50% of the original value and the material was able to release 32 g/100 cm² of the bound water. Mechanical characteristics were not impaired compared to the native material. The process was suitable for encapsulating the highly lipophilic drugs cannabidiol and 3-O-acetyl-11-keto-β-boswellic acid and enabled their sustained release with zero order kinetics over up to 10 days. Conclusively, controlled drug release for highly lipophilic compounds within bacterial nanocellulose could be achieved using sustainable solvents for preparation.

Role of Polymers in Microfluidic Devices

Polymers are sustainable and renewable materials that are in high demand due to their excellent properties. Natural and synthetic polymers with high flexibility, good biocompatibility, good degradation rate, and stiffness are widely used for various applications, such as tissue engineering, drug delivery, and microfluidic chip fabrication. Indeed, recent advances in microfluidic technology allow the fabrication of polymeric matrix to construct microfluidic scaffolds for tissue engineering and to set up a well-controlled microenvironment for manipulating fluids and particles. In this review, polymers as materials for the fabrication of microfluidic chips have been highlighted. Successful models exploiting polymers in microfluidic devices to generate uniform particles as drug vehicles or artificial cells have been also discussed. Additionally, using polymers as bioink for 3D printing or as a matrix to functionalize the sensing surface in microfluidic devices has also been mentioned. The rapid progress made in the combination of polymers and microfluidics presents a low-cost, reproducible, and scalable approach for a promising future in the manufacturing of biomimetic scaffolds for tissue engineering.

Machine learning for microfluidic design and control

Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Controlled release of carnosine from poly(lactic-co-glycolic acid) beads using nanomechanical magnetic trigger towards the treatment of glioblastoma

Nanometer scale rods of superparamagnetic iron oxide have been encapsulated, along with the anti-cancer therapeutic carnosine, inside porous poly(lactic-co-glycolic acid) microbeads with a uniform morphology, synthesised using microfluidic arrays. The sustained and externally triggered controlled release from these vehicles was demonstrated using a rotating Halbach magnet array, quantified via liquid chromatography, and imaged in situ using magnetic resonance imaging (MRI) and scanning electron microscopy (SEM). In the absence of the external magnetic trigger, the carnosine was found to be released from the polymer in a linear profile; however, over 50% of the drug could be released within 30 minutes of exposure to the rotating magnetic field. In addition, the release of carnosine embedded on the surface of the nano-rods was delayed if it was mixed with the iron oxide nano rods before the encapsulation. These new drug delivery vesicles have the potential to pave the way towards the safe and triggered release of onsite drug delivery, as part of a theragnostic treatment for glioblastoma.

Integration of reinforcement learning to realize functional variability of microfluidic systems

In this article, we present a proof-of-concept for microfluidic systems with high functional variability using reinforcement learning. By mathematically defining the objective of tasks, we demonstrate that the system can autonomously learn to behave according to its objectives. We applied Q-learning to a peristaltic micropump and showed that two different tasks can be performed on the same platform: adjusting the flow rate of the pump and manipulating the position of the particles. First, we performed typical micropumping with flow rate control. In this task, the system is rewarded according to the deviation between the average flow rate generated by the micropump and the target value. Therefore, the objective of the system is to maintain the target flow rate via an operation of the pump. Next, we demonstrate the micromanipulation of a small object (microbead) on the same platform. The objective was to manipulate the microbead position to the target area, and the system was rewarded for the success of the task. These results confirmed that the system learned to control the flow rate and manipulate the microbead to any randomly chosen target position. In particular, the manipulation technique is a new technology that does not require the use of structures such as wells or weirs. Therefore, this concept not only adds flexibility to the system but also contributes to the development of novel control methods to realize highly versatile microfluidic systems.

Poly(Lactic Acid)-Based Microparticles for Drug Delivery Applications: An Overview of Recent Advances

The sustained release of pharmaceutical substances remains the most convenient way of drug delivery. Hence, a great variety of reports can be traced in the open literature associated with drug delivery systems (DDS). Specifically, the use of microparticle systems has received special attention during the past two decades. Polymeric microparticles (MPs) are acknowledged as very prevalent carriers toward an enhanced bio-distribution and bioavailability of both hydrophilic and lipophilic drug substances. Poly(lactic acid) (PLA), poly(lactic-co-glycolic acid) (PLGA), and their copolymers are among the most frequently used biodegradable polymers for encapsulated drugs. This review describes the current state-of-the-art research in the study of poly(lactic acid)/poly(lactic-co-glycolic acid) microparticles and PLA-copolymers with other aliphatic acids as drug delivery devices for increasing the efficiency of drug delivery, enhancing the release profile, and drug targeting of active pharmaceutical ingredients (API). Potential advances in generics and the constant discovery of therapeutic peptides will hopefully promote the success of microsphere technology.

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.

A perspective on magnetic microfluidics: Towards an intelligent future

Magnetic microfluidics has been gradually recognized as an area of its own. Both conventional microfluidic platforms have incorporated magnetic actuation for microfluidic operation and microscale object manipulation. Nonetheless, there is still much room for improvement after decades of development. In this Perspective, we first provide a quick review of existing magnetic microfluidic platforms with a focus on the magnetic tools and actuation mechanisms. Next, we discuss several emerging technologies, including magnetic microrobots, additive manufacture, and artificial intelligence, and their potential application in the future development of magnetic microfluidics. We believe that these technologies can eventually inspire highly functional magnetic tools for microfluidic manipulation and coordinated microfluidic control at the system level, which eventually drives magnetic microfluidics into an intelligent system for automated experimentation.

Exploiting machine learning for bestowing intelligence to microfluidics

Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics.

Machine learning assisted optimization of blending process of polyphenylene sulfide with elastomer using high speed twin screw extruder

Random forest regression was applied to optimize the melt-blending process of polyphenylene sulfide (PPS) with poly(ethylene-glycidyl methacrylate-methyl acrylate) (E-GMA-MA) elastomer to improve the Charpy impact strength. A training dataset was constructed using four elastomers with different GMA and MA contents by varying the elastomer content up to 20 wt% and the screw rotation speed of the extruder up to 5000 rpm at a fixed barrel temperature of 300 °C. Besides the controlled parameters, the following measured parameters were incorporated into the descriptors for the regression: motor torque, polymer pressure, and polymer temperatures monitored by infrared-ray thermometers installed at four positions (T1 to T4) as well as the melt viscosity and elastomer particle diameter of the product. The regression without prior knowledge revealed that the polymer temperature T1 just after the first kneading block is an important parameter next to the elastomer content. High impact strength required high elastomer content and T1 below 320 °C. The polymer temperature T1 was much higher than the barrel temperature and increased with the screw speed due to the heat of shear. The overheating caused thermal degradation, leading to a decrease in the melt viscosity and an increase in the particle diameter at high screw speed. We thus reduced the barrel temperature to keep T1 around 310 °C. This increased the impact strength from 58.6 kJ m⁻² as the maximum in the training dataset to 65.3 and 69.0 kJ m⁻² at elastomer contents of 20 and 30 wt%, respectively.

Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis

Artificial intelligence (AI) is an emerging technology with great potential, and its robust calculation and analysis capabilities are unmatched by traditional calculation tools. With the promotion of deep learning and open-source platforms, the threshold of AI has also become lower. Combining artificial intelligence with traditional fields to create new fields of high research and application value has become a trend. AI has been involved in many disciplines, such as medicine, materials, energy, and economics. The development of AI requires the support of many kinds of data, and microfluidic systems can often mine object data on a large scale to support AI. Due to the excellent synergy between the two technologies, excellent research results have emerged in many fields. In this review, we briefly review AI and microfluidics and introduce some applications of their combination, mainly in nanomedicine and material synthesis. Finally, we discuss the development trend of the combination of the two technologies.

High throughput production of microcapsules using microfluidics for self-healing of cementitious materials

Capsule-based self-healing of cementitious materials is an effective way of healing cracks, significantly extending the life of structures, without imposing changes due to the incorporation of capsules into products during mixing. The methodologies currently being used for the development of capsules with a liquid core as a healing agent yield a wide range of sizes and shell thicknesses for the microcapsules, preventing a detailed assessment and optimisation of the microcapsule size and its effects. Uniquely, microfluidic technology offers precise control over the size and shell thickness through the formation of double emulsions. The drawback is that only small quantities of material can be typically produced. Here, by using paralleled junctions in a microfluidic device, high throughput production of materials was achieved, focusing for the first time on self-healing of cementitious materials. A microfluidic chip was assembled with 4 channels in parallel and selected hydrophobicity for the formation of the double emulsions. A coefficient of variation below 2.5% was observed for the 4 junctions, demonstrating the formation of monodisperse capsules. The control over the size and shell thickness by adjusting the flow rates was demonstrated, yielding capsules with an outer diameter of 615-630 μm and a shell thickness varying between 50 and 127 μm. By using triethanolamine as a surfactant, capsules with an aqueous core were produced. Furthermore, by selecting PEA, an acrylate with low tensile strength, the capsules embedded in the cement paste were successfully triggered to release the healing agent by crack formation. Capsules were successfully produced continuously for 7 h, with inner and outer diameters of 500 ± 31 μm and 656 ± 9 μm at a production rate of ∼13 g h^-1 and a yield of around 80%. With these results and considering up to 6 chips in parallel, the production rate could be up to 1.5 kg per day. This demonstrates the huge potential of the microfluidic device with unique features to produce sufficiently large quantities of microcapsules for laboratory-scale assessment of self-healing performance.

Microfluidic Synthesis of Indomethacin-Loaded PLGA Microparticles Optimized by Machine Learning

Several attempts have been made to encapsulate indomethacin (IND), to control its sustained release and reduce its side effects. To develop a successful formulation, drug release from a polymeric matrix and subsequent biodegradation need to be achieved. In this study, we focus on combining microfluidic and artificial intelligence (AI) technologies, alongside using biomaterials, to generate drug-loaded polymeric microparticles (MPs). Our strategy is based on using Poly (D,L-lactide-co-glycolide) (PLGA) as a biodegradable polymer for the generation of a controlled drug delivery vehicle, with IND as an example of a poorly soluble drug, a 3D flow focusing microfluidic chip as a simple device synthesis particle, and machine learning using artificial neural networks (ANNs) as an in silico tool to generate and predict size-tunable PLGA MPs. The influence of different polymer concentrations and the flow rates of dispersed and continuous phases on PLGA droplet size prediction in a microfluidic platform were assessed. Subsequently, the developed ANN model was utilized as a quick guide to generate PLGA MPs at a desired size. After conditions optimization, IND-loaded PLGA MPs were produced, and showed larger droplet sizes than blank MPs. Further, the proposed microfluidic system is capable of producing monodisperse particles with a well-controllable shape and size. IND-loaded-PLGA MPs exhibited acceptable drug loading and encapsulation efficiency (7.79 and 62.35%, respectively) and showed sustained release, reaching approximately 80% within 9 days. Hence, combining modern technologies of machine learning and microfluidics with biomaterials can be applied to many pharmaceutical applications, as a quick, low cost, and reproducible strategy.

Comparative Evaluation of Artificial Neural Networks and Data Analysis in Predicting Liposome Size in a Periodic Disturbance Micromixer

Artificial neural networks (ANN) and data analysis (DA) are powerful tools for supporting decision-making. They are employed in diverse fields, and one of them is nanotechnology; for example, in predicting silver nanoparticles size. To our knowledge, we are the first to use ANN to predict liposome size (LZ). Liposomes are lipid nanoparticles used in different biomedical applications that can be produced in Dean-Forces-based microdevices such as the Periodic Disturbance Micromixer (PDM). In this work, ANN and DA techniques are used to build a LZ prediction model by using the most relevant variables in a PDM, the Flow Rate Radio (FRR), and the Total Flow Rate (TFR), and the temperature, solvents, and concentrations were kept constant. The ANN was designed in MATLAB and fed data from 60 experiments with 70% training, 15% validation, and 15% testing. For DA, a regression analysis was used. The model was evaluated; it showed a 0.98147 correlation coefficient for training and 0.97247 in total data compared with 0.882 obtained by DA.