Corruption and hierarchy: a replication of studies 1c and 6 of Fath & Kay 2018.

Corruption represents a complex problem firmly embedded within our societal structures, governments, and organizations. The current study aimed to build a clearer consensus on the extent to which perceptions of organizational corruption are associated with organizational hierarchy. Two high-powered close replications of studies 1c and 6 by Fath and Kay provide further evidence for the claim that taller organizational structures are associated with greater perceived potential for corruption, and that these perceptions may compromise subsequent trust-related outcomes. Our results reinforce the importance of organizational design and aim to inspire future works to consider the ways in which researchers and organizations can minimize corruption. Preregistration, data and materials can be found on the OSF: https://osf.io/zb5j2.

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.

Biological features, distribution, and conservation of the near-threatened Gangetic leaf fish Nandus nandus (Hamilton, 1822): A review.

Nandus nandus (Hamilton, 1822) is a small indigenous species (SIS) and commercially important food fish found in Bangladesh, India, Nepal, Pakistan, Myanmar, and Thailand. Concerns have arisen due to the gradual decline in its abundance, reflected by poor catches and limited availability of this species in local markets. Habitat loss and overexploitation pose significant threats to this species, leading to its classification as near threatened by the IUCN Bangladesh. Despite the absence of dedicated conservation efforts for N. nandus in Indian subcontinent, this study aims to investigate the species' biology, ecology, distribution within the country and globally, as well as its threats, conservation strategies, and research needs. Additionally, a proposed conservation framework for this near-threatened species is presented, which can be adapted for implementation in other nations facing similar challenges.

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.

Flexible, stretchable, and single-molecule-sensitive SERS-active sensor for wearable biosensing applications.

The development of wearable sensors for remote patient monitoring and personalized medicine has led to a revolution in biomedical technology. Plasmonic metasurfaces that enhance Raman scattering signals have recently gained attention as wearable sensors. However, finding a flexible, sensitive, and easy-to-fabricate metasurface has been a challenge for decades. In this paper, a novel wearable device, the flexible, stretchable, and single-molecule-sensetive SERS-active sensor, is proposed. This device offers an unprecedented SERS enhancement factor in the order of 1011, along with other long-desired characteristics for SERS applications such as a high scattering to absorption ratio (∼2.5) and a large hotspot volume (40 nm × 40 nm × 5 nm). To achieve flexibility, we use polydimethylsiloxane (PDMS) as the substrate, which is stable, transparent, and biologically compatible. Our numerical calculations show that the proposed sensor offers reliable SERS performance even under bending (up to 100° angles) or stretching (up to 50% stretch). The easy-to-fabricate and flexible nature of our sensor offers a promising avenue for developing highly sensitive wearable sensors for a range of applications, particularly in the field of personalized medicine and remote patient monitoring.

Nutritional characterization and antioxidant properties of various edible portions of Cucurbita maxima: A potential source of nutraceuticals.

Pumpkin (Cucurbita maxima) is a widely grown vegetable in Bangladesh and is known as the sole supplier of various nutrients. Many studies evidence the nutritional value of flesh and seed while peel, flower, and leaves were reported scarcely with limited information. Therefore, the study aimed to investigate the nutritional composition and antioxidant properties of flesh, peel, seed, leaves, and flowers of Cucurbita maxima. The seed had a remarkable composition of nutrients and amino acids. Flowers and leaves possessed higher content of minerals, phenols, flavonoids, carotenes, and total antioxidant activity. The order of IC50 value (peel > seed > leaves > flesh > flower) indicates higher DPPH radicals scavenging activity of the flower. Moreover, a significant positive relationship was observed among these phytochemical constituents (TPC, TFC, TCC, TAA) and DPPH radicals scavenging activity. It could be concluded that these five parts of the pumpkin plant have an intense potency to be an exigent component of functional food or medicinal herbs.

Exploring the effects of adiponectin and leptin in correlating obesity with cognitive decline: a systematic review.

Obesity and cognitive decline including dementia and Alzheimer's Disease (AD) affect millions worldwide. Several studies have shown that obese individuals suffer from cognitive decline. Here, we suggest that adiponectin and leptin, protein hormones secreted by white adipose tissue explain the relationship between obesity and cognitive decline. We systematically searched PubMed and World Health Organization (WHO) websites with the keywords obesity and dementia and compiled literature that explains how adiponectin and leptin impact obesity and cognitive decline. Full-text, free-access articles on PubMed published after 2009 have been included. Whereas articles published before 2009, books, and reports were excluded. We concentrated on mechanisms via which adiponectin and leptin affect energy expenditure, fatty acid catabolism, satiety, hunger, Body Mass Index (BMI), neurogenesis, and brain structures that lead to the development of cognitive dysfunction. Moreover, we hypothesized that adiponectin and leptin hormones explain how obesity and dementia are connected. After compiling the research studies, we summarized that adiponectin and leptin negatively correlate to BMI. Adiponectin arbitrates energy expenditure and fatty acid catabolism to prevent obesity. In the presence of adiponectin, hippocampal cells proliferate, whereas neurogenesis is reduced in its absence. However, leptin prevents obesity by promoting satiety, reducing hunger, and increasing insulin sensitivity. It also has neuroprotective effects thus reducing the risk of developing cognitive decline. So, physical exercise, diet alteration, weight reduction, adiponectin, and leptin supplementation should be carried out to protect against obesity-induced cognitive decline. Therefore, further research studies should be done in this area.

Microbial Fuel Cell Construction Features and Application for Sustainable Wastewater Treatment.

A microbial fuel cell (MFC) is a system that can generate electricity by harnessing microorganisms' metabolic activity. MFCs can be used in wastewater treatment plants since they can convert the organic matter in wastewater into electricity while also removing pollutants. The microorganisms in the anode electrode oxidize the organic matter, breaking down pollutants and generating electrons that flow through an electrical circuit to the cathode compartment. This process also generates clean water as a byproduct, which can be reused or released back into the environment. MFCs offer a more energy-efficient alternative to traditional wastewater treatment plants, as they can generate electricity from the organic matter in wastewater, offsetting the energy needs of the treatment plants. The energy requirements of conventional wastewater treatment plants can add to the overall cost of the treatment process and contribute to greenhouse gas emissions. MFCs in wastewater treatment plants can increase sustainability in wastewater treatment processes by increasing energy efficiency and reducing operational cost and greenhouse gas emissions. However, the build-up to the commercial-scale still needs a lot of study, as MFC research is still in its early stages. This study thoroughly describes the principles underlying MFCs, including their fundamental structure and types, construction materials and membrane, working mechanism, and significant process elements influencing their effectiveness in the workplace. The application of this technology in sustainable wastewater treatment, as well as the challenges involved in its widespread adoption, are discussed in this study.

The Advancement in Membrane Bioreactor (MBR) Technology toward Sustainable Industrial Wastewater Management.

The advancement in water treatment technology has revolutionized the progress of membrane bioreactor (MBR) technology in the modern era. The large space requirement, low efficiency, and high cost of the traditional activated sludge process have given the necessary space for the MBR system to come into action. The conventional activated sludge (CAS) process and tertiary filtration can be replaced by immersed and side-stream MBR. This article outlines the historical advancement of the MBR process in the treatment of industrial and municipal wastewaters. The structural features and design parameters of MBR, e.g., membrane surface properties, permeate flux, retention time, pH, alkalinity, temperature, cleaning frequency, etc., highly influence the efficiency of the MBR process. The submerged MBR can handle lower permeate flux (requires less power), whereas the side-stream MBR can handle higher permeate flux (requires more power). However, MBR has some operational issues with conventional water treatment technologies. The quality of sludge, equipment requirements, and fouling are major drawbacks of the MBR process. This review paper also deals with the approach to address these constraints. However, given the energy limitations, climatic changes, and resource depletion, conventional wastewater treatment systems face significant obstacles. When compared with CAS, MBR has better permeate quality, simpler operational management, and a reduced footprint requirement. Thus, for sustainable water treatment, MBR can be an efficient tool.

A review of fish diversity, decline drivers, and management of the Tanguar Haor ecosystem: A globally recognized Ramsar site in Bangladesh.

Tanguar Haor (TH), an ecologically critical area (ECA) and a Ramsar site of worldwide significance, is an essential wetland ecosystem for the Bangladesh's economic, ecological, social, and cultural aspects. Fish, aquatic plants, amphibians, reptiles, birds, and mammals are notable among the floral and faunal compositions found in this haor. Unfortunately, unsustainable exploitation of its natural resources poses a serious threat to the TH ecosystem. Therefore, the broad objective of this study was to review the status of fish biodiversity along with the driving factors of biodiversity loss and the management issues of the TH ecosystem. A total of 143 species of fishes (137 indigenous and 6 exotic) under 35 families, and 12 orders were documented during the last two decades. Species diversity of the haor has been changed over time due to the effects of climatic, anthropogenic, socioeconomic, and policy related drivers. Furthermore, high dependency on fisheries resources, poverty, and the lack of empowerment to manage the TH fishery were responsible for fish diversity decline. Therefore, ecosystem based co-management through active participation of local community, establishment of balanced fishing tactics, and strengthening alternative livelihoods for highly depended poor harvesters are strongly recommended for the proper management of this valued wetland ecosystem. Furthermore, this review proposes immediate and useful conservation initiatives for the studied wetlands, including comprehensive stock assessment, establishment of gene banks and fish sanctuaries, a combination of input and output control, and regulation with the ECA and RAMSAR guidelines.

Vulnerability for Respiratory Infections in Asthma Patients: A Systematic Review.

Asthma is a non-communicable and long-term condition affecting children and adults. The air passages in the lungs become narrow due to inflammation and tightening of the muscles around the small airways. Symptoms of asthma are intermittent and include cough, wheeze, shortness of breath, and chest tightness. Asthma is very often underdiagnosed and under-treated in many regions, especially in developing countries. While many studies show that viral infections can precipitate asthmatic attacks, very few studies have been conducted to see if history or current asthmatic attack increases the risk of viral infections. Our study aims to determine the predisposition of asthmatics to develop various viral infections and susceptibility toward certain viruses that cause upper respiratory tract infections. We performed a literature review of both published and unpublished articles. We included case reports, case series, reviews, clinical trials, cohort, and case-control studies, written only in English. Commentaries, letters to editors, and book chapters were excluded. Our initial search yielded 948 articles, of which 826 were rejected either because they were irrelevant or because they did not meet our inclusion criteria. We finally screened 122 abstracts and identified 24 relevant articles. People with a history of asthma have an abnormal innate immune response, making them potentially slower in clearing the infection and susceptible to both infections and virus-induced cell cytotoxicity. Also, in these studies, deficiencies in the interferon alpha response of peripheral blood mononuclear cells and plasmacytoid dendritic cells have been observed in asthmatics, both adults and children. Asthmatics with a viral infection usually present with an acute exacerbation of asthma, represented by dyspnea and cough, with other prodromal symptoms including vomiting and general malaise. The review includes an update on the relevance of dysregulated immune pathways in causing viral infections in asthmatic populations. It focuses on the evidence to suggest that people with asthma are at increased risk of viral infection, and viral infections in turn are known to precipitate and worsen the asthmatic status, making this a vicious cycle. The authors also suggest that further studies be undertaken to elucidate the pathophysiology and identify the critical therapeutic steps to break this vicious cycle and improve the quality of life for people with asthma.

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.

Empagliflozin Ameliorates Progression From Prediabetes to Diabetes and Improves Hepatic Lipid Metabolism: A Systematic Review.

Diabetes mellitus (DM) and hepatic steatosis are two of the most common metabolic syndromes that affect the health of people globally. Empagliflozin (EMPA) is a promising drug of choice for the diabetic population. Recent studies have shown its beneficial effects not only on diabetic patients but also on patients suffering from cardiac, hepatic, neurological, or pancreatic anomalies. In this paper, we systematically searched electronic databases to compile literature that focuses on EMPA's effect on the prediabetic population, diabetic population, and hepatic lipid metabolism. We focus on the mechanism of EMPA, specifically by which it increases insulin sensitivity and fat browning and reduces fat accumulation. Overall, we hypothesized that by its effect on weight loss and reducing inflammatory markers and insulin resistance (IR), EMPA decreases the rate of prediabetes to diabetes conversion. We concluded that by improving hepatic and serum triglyceride, decreasing visceral fat, and its positive impact on hepatic steatosis, the drug improves hepatic lipid metabolism. Further research should be done on this matter.

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.

Impacts of brush pile fishing on fish biodiversity: A case study of the Shari-Goyain River in Bangladesh.

Brush pile (katha), a fish aggregating device, has been widely used in the Shari-Goyain River since 2003 to congregate fish for easier catch. Katha is usually used during the winter season when the water depth decreases. Hence, this experiment was conducted from November 2018 to March 2019 on katha fishing to investigate its status and impacts on fisheries resources of the Shari-Goyain River in the Sylhet district of Bangladesh. The study was based on the hypothesis that katha fishing might have detrimental impacts to fish biodiversity and production. Data were obtained through a questionnaire-based survey, personal interviews, catch assessment (CA), focus group discussions, and key informant interviews. A total of 54 species were documented, including two exotic fish species (tilapia and common carp) and 3 species of prawn during harvesting of the kathas. The catch per unit effort (CPUE) (kg/gear/ha/person/hour) was the highest in December (1.13 ± 0.37), followed by November (1.06 ± 0.40), January (0.80 ± 0.25), February (0.71 ± 0.23), and March (0.52 ± 0.21). The catch per unit area (CPUA) (kg/ha) was the highest in November (264.66 ± 18.21), followed by December (205.05 ± 27.77), January (175.02 ± 76.04), February (147.73 ± 52.11), and March (102.08 ± 41.04) where significant differences (p < 0.05) among the months were observed. Average catch per katha in a month ranged from 41.09 ± 16.11 to 12.42 ± 5.89 kg, with a mean of 24.29 ± 11.08 kg, and a significant decrease in average catch was observed with the progression of months. The most species richness was noticed in December (38), followed by November (35), January (34), February (28), and March (25). Siluriformes (39.123%) was the most dominant order, followed by Cypriniformes (33.956%), Decapoda (14.661%), and Ovalentaria (3.278%). According to the CA and respondents' perception, indiscriminate harvesting of fish by katha fishing can be a cause of fish biodiversity loss as it reduces open water catches, total production, and disturbs the ecosystem. From the research findings, it is suggested that katha fishing should be stopped for sustainable management and conservation of fisheries resources in the Shari-Goyain River. Research on the effects of katha fishing should be conducted in other open waters of Bangladesh where this type of fishing is common.

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.