High throughput microfluidics-based synthesis of PEGylated liposomes for precise size control and efficient drug encapsulation.

The low scalability and reproducibility of existing synthesis methods have hindered the translation of liposome nanoparticles as carriers for targeted drug delivery from conventional laboratory techniques to mass production. To this end, in this study, we present a high-throughput microfluidics-based approach for the synthesis of PEGylated liposomes with a primary focus on achieving precise size control and efficient encapsulation of hydrophobic drug molecules. In this platform, liposomes were self-assembled through a controllable mixing of lipids (EYPC, cholesterol, and DSPE-PEG 2000) dissolved in ethanol and an aqueous solution. The key parameters, including the chip design, total flow rate, flow rate ratio, lipid concentrations, as well as variations in buffer (HEPES and NaCl) and solvent composition (commercial and reagent-grade ethanol) were explored in detail. Through comprehensive parametric studies, we gained valuable insights into the influence of these variables on the size distribution of liposomes and succeeded in producing highly reproducible liposomes ranging from approximately 60 nm (corresponding to small unilamellar vesicles) to 150 nm (representing large unilamellar vesicles), all while maintaining a polydispersity index (PDI) of less than 0.2. To assess the encapsulation efficiency of hydrophobic drug molecules, Nile red (NR) was employed as a surrogate. We meticulously examined the impact of NR concentration on the drug encapsulation process, resulting in up to 74% drug encapsulation efficiency within the PEGylated liposomes. This research offers crucial advances in liposome synthesis and drug delivery, providing a high-throughput, controllable method for PEGylated liposomes with potential in pharmaceutical and biomedical fields.

Modeling the neuroimmune system in Alzheimer's and Parkinson's diseases.

Parkinson's disease (PD) and Alzheimer's disease (AD) are neurodegenerative disorders caused by the interaction of genetic, environmental, and familial factors. These diseases have distinct pathologies and symptoms that are linked to specific cell populations in the brain. Notably, the immune system has been implicated in both diseases, with a particular focus on the dysfunction of microglia, the brain's resident immune cells, contributing to neuronal loss and exacerbating symptoms. Researchers use models of the neuroimmune system to gain a deeper understanding of the physiological and biological aspects of these neurodegenerative diseases and how they progress. Several in vitro and in vivo models, including 2D cultures and animal models, have been utilized. Recently, advancements have been made in optimizing these existing models and developing 3D models and organ-on-a-chip systems, holding tremendous promise in accurately mimicking the intricate intracellular environment. As a result, these models represent a crucial breakthrough in the transformation of current treatments for PD and AD by offering potential for conducting long-term disease-based modeling for therapeutic testing, reducing reliance on animal models, and significantly improving cell viability compared to conventional 2D models. The application of 3D and organ-on-a-chip models in neurodegenerative disease research marks a prosperous step forward, providing a more realistic representation of the complex interactions within the neuroimmune system. Ultimately, these refined models of the neuroimmune system aim to aid in the quest to combat and mitigate the impact of debilitating neuroimmune diseases on patients and their families.

Microfluidic integrated gas sensors for smart analyte detection: a comprehensive review.

The utilization of gas sensors has the potential to enhance worker safety, mitigate environmental issues, and enable early diagnosis of chronic diseases. However, traditional sensors designed for such applications are often bulky, expensive, difficult to operate, and require large sample volumes. By employing microfluidic technology to miniaturize gas sensors, we can address these challenges and usher in a new era of gas sensors suitable for point-of-care and point-of-use applications. In this review paper, we systematically categorize microfluidic gas sensors according to their applications in safety, biomedical, and environmental contexts. Furthermore, we delve into the integration of various types of gas sensors, such as optical, chemical, and physical sensors, within microfluidic platforms, highlighting the resultant enhancements in performance within these domains.

Highly selective molecularly imprinted polymer nanoparticles (MIP NPs)-based microfluidic gas sensor for tetrahydrocannabinol (THC) detection.

A highly selective microfluidic integrated metal oxide gas sensor for THC detection is reported based on MIP nanoparticles (MIP NPs). We synthesized MIP NPs with THC recognition sites and coated them on a 3D-printed microfluidic channel surface. The sensitivity and selectivity of coated microfluidic integrated gas sensors were evaluated by exposure to THC, cannabidiol (CBD), methanol, and ethanol analytes in 300-700 ppm at 300 °C. For comparison, reference signals were obtained from a microfluidic channel coated with nonimprinted polymers (NIP NPs). The MIP and NIP NPs were characterized using scanning electron microscopy (SEM) and Raman spectroscopy. MIP and NIP NPs channels response data were combined and classified with 96.3% accuracy using the Fine KNN classification model in MATLAB R2021b Classification Learner App. Compared to the MIP NPs coated channel, the NIP NPs channel had poor selectivity towards THC, demonstrating that the THC recognition sites in the MIP structure enabled selective detection of THC. The findings demonstrated that the recognition sites of MIP NPs properly captured THC molecules, enabling the selective detection of THC compared to CBD, methanol, and ethanol.

Microplastic in situ detection based on a portable triboelectric microfluidic sensor.

Microplastics (MPs) are a size-based category of plastic pollutants between 1 µm to 5 mm in particle size that are ubiquitous in land and water resources due to anthropogenic activity. Current methodologies for MPs identification and characterization require laboratory instruments and specialized training. In the present study, a miniaturized microfluidic triboelectric sensor (M-TES) is proposed for the rapid detection of MPs from water samples. The viability and versatility of this device is illustrated for in situ measurement of the size and concentration of polystyrene (PS) micro-particles in water. The M-TES sensor relies on inducing and measuring electrical charges generated by microplastic flow within water droplets passing through a microfluidic channel. The experimental samples encompass pure polystyrene (PS) microparticles ranging from 500 nm to 10 µm, as well as actual samples collected from a coffee machine. The results reveal that the sensor's response exhibits a linear correlation with the increase in both microplastic size and concentration. The proposed sensing system could distinguish between microplastics of different sizes and concentrations. The results demonstrate the applicability of the M-TES in the field of sensors for environmental monitoring.

Magnetic Levitation System Isolates and Purifies Airborne Viruses.

Detection of viable viruses in the air is critical in order to determine the level of risk associated with the airborne diffusion of viruses. Different methods have been developed for the isolation, purification, and detection of viable airborne viruses, but they require an extensive processing time and often present limitations including low physical efficiency (i.e., the amount of collected viruses), low biological efficiency (i.e., the number of viable viruses), or a combination of all. To mitigate such limitations, we have employed an efficient technique based on the magnetic levitation (Maglev) technique with a paramagnetic solution and successfully identified distinct variations in levitation and density characteristics among bacteria (Escherichia coli), phages (MS2), and human viruses (SARS-CoV-2 and influenza H1N1). Notably, the Maglev approach enabled a significant enrichment of viable airborne viruses in air samples. Furthermore, the enriched viruses obtained through Maglev exhibited high purity, rendering them suitable for direct utilization in subsequent analyses such as reverse transcription-polymerase chain reaction (RT-PCR) or colorimetric assays. The system is portable, easy to use, and cost-efficient and can potentially provide proactive surveillance data for monitoring future outbreaks of airborne infectious diseases and allow for the induction of various preventative and mitigative measures.

Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors.

Alternative fuel sources, such as hydrogen-enriched natural gas (HENG), are highly sought after by governments globally for lowering carbon emissions. Consequently, the recognition of hydrogen as a valuable zero-emission energy carrier has increased, resulting in many countries attempting to enrich natural gas with hydrogen; however, there are rising concerns over the safe use, storage, and transport of H2 due to its characteristics such as flammability, combustion, and explosivity at low concentrations (4 vol%), requiring highly sensitive and selective sensors for safety monitoring. Microfluidic-based metal-oxide-semiconducting (MOS) gas sensors are strong tools for detecting lower levels of natural gas elements; however, their working mechanism results in a lack of real-time analysis techniques to identify the exact concentration of the present gases. Current advanced machine learning models, such as deep learning, require large datasets for training. Moreover, such models perform poorly in data distribution shifts such as instrumental variation. To address this problem, we proposed a Sparse Autoencoder-based Transfer Learning (SAE-TL) framework for estimating the hydrogen gas concentration in HENG mixtures using limited datasets from a 3D printed microfluidic detector coupled with two commercial MOS sensors. Our framework detects concentrations of simulated HENG based on time-series data collected from a cost-effective microfluidic-based detector. This modular gas detector houses metal-oxide-semiconducting (MOS) gas sensors in a microchannel with coated walls, which provides selectivity based on the diffusion pace of different gases. We achieve a dominant performance with the SAE-TL framework compared to typical ML models (94% R-squared). The framework is implementable in real-world applications for fast adaptation of the predictive models to new types of MOS sensor responses.

Bio-macromolecular design roadmap towards tough bioadhesives.

Emerging sutureless wound-closure techniques have led to paradigm shifts in wound management. State-of-the-art biomaterials offer biocompatible and biodegradable platforms enabling high cohesion (toughness) and adhesion for rapid bleeding control as well as robust attachment of implantable devices. Tough bioadhesion stems from the synergistic contributions of cohesive and adhesive interactions. This Review provides a biomacromolecular design roadmap for the development of tough adhesive surgical sealants. We discuss a library of materials and methods to introduce toughness and adhesion to biomaterials. Intrinsically tough and elastic polymers are leveraged primarily by introducing strong but dynamic inter- and intramolecular interactions either through polymer chain design or using crosslink regulating additives. In addition, many efforts have been made to promote underwater adhesion via covalent/noncovalent bonds, or through micro/macro-interlock mechanisms at the tissue interfaces. The materials settings and functional additives for this purpose and the related characterization methods are reviewed. Measurements and reporting needs for fair comparisons of different materials and their properties are discussed. Finally, future directions and further research opportunities for developing tough bioadhesive surgical sealants are highlighted.

Selective monitoring of natural gas sulphur-based odorant mixture of t-butyl mercaptan and methyl ethyl sulphide using an array of microfluidic gas sensors.

A natural gas (NG) odorization system requires continuous monitoring as well as an optimal injection to satisfy the odorization guidelines, minimize over-odorization, and prevent hazardous gas leaks. NG consists of hydrocarbons such as methane, odorants such as tert-butyl mercaptan, and other sulphur-based VOCs such as hydrogen sulphide; therefore, selectivity is paramount for the reliable and accurate monitoring of odorants. In this study, we developed a portable device integrated with an array of five different sensors to detect a mixture of tert-butyl mercaptan and methyl ethyl sulphide for a concentration range of 1 ppm to 10 ppm. A machine learning model was developed to predict the presence and concentration of NG odorants from the sensor data. The best-performing sensors in the array achieved high sensitivity and selectivity indicators (measured using the Davies-Bouldin index) of 0.3667 (1/ppm) and 0.125, respectively. The sensor system achieved a classification accuracy of 98.75% between NG odorants and hydrogen sulphide, with an overall Mean Squared Error (MSE) and R2 error (for the regression model) of 0.50 and 95.16%. These results indicate that the developed portable device and the machine learning model have promising applications for the selective monitoring of NG odorants.

Machine Learning-Aided Microdroplets Breakup Characteristic Prediction in Flow-Focusing Microdevices by Incorporating Variations of Cross-Flow Tilt Angles.

Controlling droplet breakup characteristics such as size, frequency, regime, and droplet quality within flow-focusing microfluidic devices is critical for different biomedical applications of droplet microfluidics such as drug delivery, biosensing, and nanomaterial preparation. The development of a prediction platform capable of forecasting droplet breakup characteristics can significantly improve the iterative design and fabrication processes required for achieving desired performance. The present study aims to develop a multipurpose platform capable of predicting the working conditions of user-specific droplet size and frequency and reporting the quality of the generated droplets, regime, and hydrodynamical breakup characteristics in flow-focusing microdevices with different cross-junction tilt angles. Four different neural network-based prediction platforms were compared to accurately estimate capsule size, generation rate, uniformity, and circle metric. The trained capsule size and frequency networks were optimized using the heuristic optimization approach for establishing the Pareto optimal solution plot. To investigate the transition of the droplet generation regime (i.e., squeezing, dripping, and jetting), two different classification models (LDA and MLP) were developed and compared in terms of their prediction accuracy. The MLP model outperformed the LDA model with a cross-validation measure evaluated as 97.85%, demonstrating that the droplet quality and regime prediction models can provide an engineering judgment for the decision maker to choose between the suggested solutions on the Pareto front. The study followed a comprehensive hydrodynamical analysis of the junction angle effect on the dispersed thread formation, pressure, and velocity domains in the orifice.

Experimental comparison of direct and indirect aptamer-based biochemical functionalization of electrolyte-gated graphene field-effect transistors for biosensing applications.

Aptamer-based electrolyte-gated graphene field-effect transistor (EGFET) biosensors have gained considerable attention because of their rapidity and accuracy in terms of quantification of a wide range of biomarkers. Functionalization of the graphene channel of EGFETs with aptamer biorecognition elements (BREs) is a crucial step in fabrication of EGFET aptasensors. This paper presents a comprehensive comparison of commonly used biochemical functionalization approaches applied for preparation of sensing films in EGFET aptasensors, namely indirect and direct immobilization of BREs. This study is the first of its kind to experimentally compare the two BREs immobilization approaches in terms of their effects on the carrier mobility of the monolayer graphene channel and their suitability for sensing applications. Both approaches can preserve and even improve the carrier mobility of bare graphene channel and hence the sensitivity of the EGFET; however, the direct BREs immobilization method was selected to develop an aptameric EGFET biosensor as this method enables simpler and more efficient preparation of the graphene-based aptameric sensing film. The utility of the prepared EGFET aptasensor is demonstrated through detection of tumor necrosis factor-α (TNF-α), an important inflammatory biomarker. The direct BREs immobilization approach is applied to develop an EGFET aptasensor to measure TNF-α in a detection range from 10 pg/ml to 10 ng/ml, representative of its physiological level in human sweat, as a non-invasively accessible biofluid. The outstanding sensing performance of the developed TNF-α EGFET aptasensor based on direct BREs immobilization can pave the way for development of graphene biosensors.

Properties and Applications of Graphene and Its Derivatives in Biosensors for Cancer Detection: A Comprehensive Review.

Cancer is one of the deadliest diseases worldwide, and there is a critical need for diagnostic platforms for applications in early cancer detection. The diagnosis of cancer can be made by identifying abnormal cell characteristics such as functional changes, a number of vital proteins in the body, abnormal genetic mutations and structural changes, and so on. Identifying biomarker candidates such as DNA, RNA, mRNA, aptamers, metabolomic biomolecules, enzymes, and proteins is one of the most important challenges. In order to eliminate such challenges, emerging biomarkers can be identified by designing a suitable biosensor. One of the most powerful technologies in development is biosensor technology based on nanostructures. Recently, graphene and its derivatives have been used for diverse diagnostic and therapeutic approaches. Graphene-based biosensors have exhibited significant performance with excellent sensitivity, selectivity, stability, and a wide detection range. In this review, the principle of technology, advances, and challenges in graphene-based biosensors such as field-effect transistors (FET), fluorescence sensors, SPR biosensors, and electrochemical biosensors to detect different cancer cells is systematically discussed. Additionally, we provide an outlook on the properties, applications, and challenges of graphene and its derivatives, such as Graphene Oxide (GO), Reduced Graphene Oxide (RGO), and Graphene Quantum Dots (GQDs), in early cancer detection by nanobiosensors.

Neural Network-Based Optimization of an Acousto Microfluidic System for Submicron Bioparticle Separation.

The advancement in microfluidics has provided an excellent opportunity for shifting from conventional sub-micron-sized isolation and purification methods to more robust and cost-effective lab-on-chip platforms. The acoustic-driven separation approach applies differential forces acting on target particles, guiding them towards different paths in a label-free and biocompatible manner. The main challenges in designing the acoustofluidic-based isolation platforms are minimizing the reflected radio frequency signal power to achieve the highest acoustic radiation force acting on micro/nano-sized particles and tuning the bandwidth of the acoustic resonator in an acceptable range for efficient size-based binning of particles. Due to the complexity of the physics involved in acoustic-based separations, the current existing lack in performance predictive understanding makes designing these miniature systems iterative and resource-intensive. This study introduces a unique approach for design automation of acoustofluidic devices by integrating the machine learning and multi-objective heuristic optimization approaches. First, a neural network-based prediction platform was developed to predict the resonator's frequency response according to different geometrical configurations of interdigitated transducers In the next step, the multi-objective optimization approach was executed for extracting the optimum design features for maximum possible device performance according to decision-maker criteria. The results show that the proposed methodology can significantly improve the fine-tuned IDT designs with minimum power loss and maximum working frequency range. The examination of the power loss and bandwidth on the alternation and distribution of the acoustic pressure inside the microfluidic channel was carried out by conducting a 3D finite element-based simulation. The proposed methodology improves the performance of the acoustic transducer by overcoming the constraints related to bandwidth operation, the magnitude of acoustic radiation force on particles, and the distribution of pressure acoustic inside the microchannel.

On-chip-based electrochemical biosensor for the sensitive and label-free detection of Cryptosporidium.

Cryptosporidium, an intestinal protozoan pathogen, is one of the leading causes of death in children and diarrhea in healthy adults. Detection of Cryptosporidium has become a high priority to prevent potential outbreaks. In this paper, a simple, easy to fabricate, and cost-effective on-chip-based electrochemical biosensor has been developed for the sensitive and label-free detection of Cryptosporidium oocysts in water samples. The sensor was fabricated using standard lithography using a mask with a 3-electrode design and modified by self-assembling a hybrid of a thiolated protein/G and the specific anti-Cryptosporidium monoclonal antibodies (IgG3). The electrochemical impedance spectroscopy (EIS) was employed to quantitate C. parvum in the range of 0 to 300 oocysts, with a detection limit of approximately 20 oocysts/5 µL. The high sensitivity and specificity of the developed label-free electrochemical biosensor suggest that this novel platform is a significant step towards the development of fast, real-time, inexpensive and label-free sensing tool for early warning and immediate on-site detection of C. parvum oocysts in water samples, as compared to the traditional methods (such as PCR and microscopy). Furthermore, under optimized conditions, this label-free biosensor can be extended to detect other analytes and biomarkers for environmental and biomedical analyses.

A review on 3D printing functional brain model.

Modern neuroscience increasingly relies on 3D models to study neural circuitry, nerve regeneration, and neural disease. Several different biofabrication approaches have been explored to create 3D neural tissue model structures. Among them, 3D bioprinting has shown to have great potential to emerge as a high-throughput/high precision biofabrication strategy that can address the growing need for 3D neural models. Here, we have reviewed the design principles for neural tissue engineering. The main challenge to adapt printing technologies for biofabrication of neural tissue models is the development of neural bioink, i.e., a biomaterial with printability and gelation properties and also suitable for neural tissue culture. This review shines light on a vast range of biomaterials as well as the fundamentals of 3D neural tissue printing. Also, advances in 3D bioprinting technologies are reviewed especially for bioprinted neural models. Finally, the techniques used to evaluate the fabricated 2D and 3D neural models are discussed and compared in terms of feasibility and functionality.

Selective detection of VOCs using microfluidic gas sensor with embedded cylindrical microfeatures coated with graphene oxide.

Volatile organic compounds (VOCs) are major environmental pollutants. Exposure to VOCs has been associated with adverse health outcomes. The monitoring of hazardous VOCs is a vital step towards identifying their presence and preventing the risk of acute or chronic exposure and polluting the environment. One of the challenges associated with monitoring VOCs is selectivity of the sensor. Microfluidic gas sensors offer selective and sensitive detection capabilities that have been recently applied for detection of VOCs. In this study, we achieve improved selectivity for detection of a range of VOCs by adding micro- and nanofeatures to the microchannel of microfluidic gas sensors. First, microfeatures are embedded into the microchannel and their geometries are optimized using Taguchi design of experiment method. In the next step the microfeatures embedded microchannel is coated with graphene oxide, to increase the surface to volume ratio by introducing nanofeatures to the surfaces. The nano- and microfeatures are characterized by SEM, XPS, and water contact angle measurement. Finally, the changes in the sensor response are compared to plain microfluidic gas sensor, the results show an average of 64.4% and 120.9% improvement in the selectivity of the sensor with microfeatures and both nano- and microfeatures, respectively.

Comprehensive review of conventional and state-of-the-art detection methods of Cryptosporidium.

Cryptosporidium is a critical waterborne protozoan pathogen found in water resources that have been a major cause of death and serious illnesses worldwide, costing millions of dollars annually for its detection and treatment. Over the past several decades, substantial efforts have been made towards developing techniques for the detection of Cryptosporidium. Early diagnostic techniques were established based on the existing tools in laboratories, such as microscopes. Advancements in fluorescence microscopy, immunological, and molecular techniques have led to the development of several kits for the detection of Cryptosporidium spp. However, these methods have several limitations, such as long processing times, large sample volumes, the requirement for bulky and expensive laboratory tools, and the high cost of reagents. There is an urgent need to improve these existing techniques and develop low-cost, portable and rapid detection tools for applications in the water quality industry. In this review, we compare recent advances in nanotechnology, biosensing and microfluidics that have facilitated the development of sophisticated tools for the detection of Cryptosporidium spp.Finally, we highlight the advantages and disadvantages, of these state-of-the-art detection methods compared to current analytical methodologies and discuss the need for future developments to improve such methods for detecting Cryptosporidium in the water supply chain to enable real-time and on-site monitoring in water resources and remote areas.

Exhaled Breath Analysis for Diabetes Diagnosis and Monitoring: Relevance, Challenges and Possibilities.

With the global population prevalence of diabetes surpassing 463 million cases in 2019 and diabetes leading to millions of deaths each year, there is a critical need for feasible, rapid, and non-invasive methodologies for continuous blood glucose monitoring in contrast to the current procedures that are either invasive, complicated, or expensive. Breath analysis is a viable methodology for non-invasive diabetes management owing to its potential for multiple disease diagnoses, the nominal requirement of sample processing, and immense sample accessibility; however, the development of functional commercial sensors is challenging due to the low concentration of volatile organic compounds (VOCs) present in exhaled breath and the confounding factors influencing the exhaled breath profile. Given the complexity of the topic and the skyrocketing spread of diabetes, a multifarious review of exhaled breath analysis for diabetes monitoring is essential to track the technological progress in the field and comprehend the obstacles in developing a breath analysis-based diabetes management system. In this review, we consolidate the relevance of exhaled breath analysis through a critical assessment of current technologies and recent advancements in sensing methods to address the shortcomings associated with blood glucose monitoring. We provide a detailed assessment of the intricacies involved in the development of non-invasive diabetes monitoring devices. In addition, we spotlight the need to consider breath biomarker clusters as opposed to standalone biomarkers for the clinical applicability of exhaled breath monitoring. We present potential VOC clusters suitable for diabetes management and highlight the recent buildout of breath sensing methodologies, focusing on novel sensing materials and transduction mechanisms. Finally, we portray a multifaceted comparison of exhaled breath analysis for diabetes monitoring and highlight remaining challenges on the path to realizing breath analysis as a non-invasive healthcare approach.

Portable on-chip colorimetric biosensing platform integrated with a smartphone for label/PCR-free detection of Cryptosporidium RNA.

Cryptosporidium, a protozoan pathogen, is a leading threat to public health and the economy. Herein, we report the development of a portable, colorimetric biosensing platform for the sensitive, selective and label/PCR-free detection of Cryptosporidium RNA using oligonucleotides modified gold nanoparticles (AuNPs). A pair of specific thiolated oligonucleotides, complementary to adjacent sequences on Cryptosporidium RNA, were attached to AuNPs. The need for expensive laboratory-based equipment was eliminated by performing the colorimetric assay on a micro-fabricated chip in a 3D-printed holder assembly. A smartphone camera was used to capture an image of the color change for quantitative analysis. The detection was based on the aggregation of the gold nanoparticles due to the hybridization between the complementary Cryptosporidium RNA and the oligonucleotides immobilized on the AuNPs surface. In the complementary RNA's presence, a distinctive color change of the AuNPs (from red to blue) was observed by the naked eye. However, in the presence of non-complementary RNA, no color change was observed. The sensing platform showed wide linear responses between 5 and 100 µM with a low detection limit of 5 µM of Cryptosporidium RNA. Additionally, the sensor developed here can provide information about different Cryptosporidium species present in water resources. This cost-effective, easy-to-use, portable and smartphone integrated on-chip colorimetric biosensor has great potential to be used for real-time and portable POC pathogen monitoring and molecular diagnostics.

Microfluidic On-Chip Production of Alginate Hydrogels Using Double Coflow Geometry.

Microfluidic on-chip production of microgels employing external gelation has numerous biological and pharmaceutical applications, particularly for the encapsulation of delicate cargos; however, the on-chip production of microgels in microfluidic devices can be challenging due to problems such as clogging caused by accelerated progress in precursor solution viscosity. Here, we introduce a novel microfluidic design incorporating two consecutive coflow geometries for microfluidic droplet generation. A shielding oil phase is employed to avoid emulsification and gelation stages from occurring simultaneously, thereby preventing clogging. The results revealed that the microfluidic device could generate highly monodispersed spherical droplets (coefficient of variation < 3%) with an average diameter in the range of 60-200 µm. Additionally, it was demonstrated that the device could appropriately create a shelter of the oil phase around the inner aqueous phase regardless of the droplet formation regime and flow conditions. The ability of the proposed microfluidic device in the generation of microgels was validated by producing alginate microgels utilizing an aqueous solution of calcium chloride as the continuous phase.