A region-resolved proteomic map of the human brain enabled by high-throughput proteomics.

Substantial efforts are underway to deepen our understanding of human brain morphology, structure, and function using high-resolution imaging as well as high-content molecular profiling technologies. The current work adds to these approaches by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 11,000 proteins. This was enabled by the optimization, characterization, and implementation of a high-sensitivity and high-throughput microflow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing more than 2,000 consecutive samples prepared from formalin-fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual markers for specific areas. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built R Shiny app (https://brain-region-atlas.proteomics.ls.tum.de).

Hyphenation of microflow chromatography with electrospray ionization mass spectrometry for bioanalytical applications focusing on low molecular weight compounds: A tutorial review.

Benefits of miniaturized chromatography with various detection modes, such as increased sensitivity, chromatographic efficiency, and speed, were recognized nearly 50 years ago. Over the past two decades, this approach has experienced rapid growth, driven by the emergence of mass spectrometry applications serving -omics sciences and the need for analyzing minute volumes of precious samples with ever higher sensitivity. While nanoscale liquid chromatography (flow rates <1 µL/min) has gained widespread recognition in proteomics, the adoption of microscale setups (flow rates ranging from 1 to 100 µL/min) for low molecular weight compound applications, including metabolomics, has been surprisingly slow, despite the inherent advantages of the approach. Highly heterogeneous matrices and chemical structures accompanied by a relative lack of options for both selective sample preparation and user-friendly equipment are usually reported as major hindrances. To facilitate the wider implementation of microscale analyses, we present here a comprehensive tutorial encompassing important theoretical and practical considerations. We provide fundamental principles in micro-chromatography and guide the reader through the main elements of a microflow workflow, from LC pumps to ionization devices. Finally, based on both our literature overview and experience, illustrated by some in-house data, we highlight the critical importance of the ionization source design and its careful optimization to achieve significant sensitivity improvement.

Unified Workflow for the Rapid and In-Depth Characterization of Bacterial Proteomes.

Bacteria are the most abundant and diverse organisms among the kingdoms of life. Due to this excessive variance, finding a unified, comprehensive, and safe workflow for quantitative bacterial proteomics is challenging. In this study, we have systematically evaluated and optimized sample preparation, mass spectrometric data acquisition, and data analysis strategies in bacterial proteomics. We investigated workflow performances on six representative species with highly different physiologic properties to mimic bacterial diversity. The best sample preparation strategy was a cell lysis protocol in 100% trifluoroacetic acid followed by an in-solution digest. Peptides were separated on a 30-min linear microflow liquid chromatography gradient and analyzed in data-independent acquisition mode. Data analysis was performed with DIA-NN using a predicted spectral library. Performance was evaluated according to the number of identified proteins, quantitative precision, throughput, costs, and biological safety. With this rapid workflow, over 40% of all encoded genes were detected per bacterial species. We demonstrated the general applicability of our workflow on a set of 23 taxonomically and physiologically diverse bacterial species. We could confidently identify over 45,000 proteins in the combined dataset, of which 30,000 have not been experimentally validated before. Our work thereby provides a valuable resource for the microbial scientific community. Finally, we grew Escherichia coli and Bacillus cereus in replicates under 12 different cultivation conditions to demonstrate the high-throughput suitability of the workflow. The proteomic workflow we present in this manuscript does not require any specialized equipment or commercial software and can be easily applied by other laboratories to support and accelerate the proteomic exploration of the bacterial kingdom.

High-Throughput Proteomics and Phosphoproteomics of Rat Tissues Using Microflow Zeno SWATH.

High-throughput tissue proteomics has great potential in the advancement of precision medicine. Here, we investigated the combined sensitivity of trap-elute microflow liquid chromatography with a ZenoTOF for DIA proteomics and phosphoproteomics. Method optimization was conducted on HEK293T cell lines to determine the optimal variable window size, MS2 accumulation time and gradient length. The ZenoTOF 7600 was then compared to the previous generation TripleTOF 6600 using eight rat organs, finding up to 23% more proteins using a fifth of the sample load and a third of the instrument time. Spectral reference libraries generated from Zeno SWATH data in FragPipe (MSFragger-DIA/DIA-NN) contained 4 times more fragment ions than the DIA-NN only library and quantified more proteins. Replicate single-shot phosphopeptide enrichments of 50-100 µg of rat tryptic peptide were analyzed by microflow HPLC using Zeno SWATH without fractionation. Using Spectronaut we quantified a shallow phosphoproteome containing 1000-3000 phosphoprecursors per organ. Promisingly, clear hierarchical clustering of organs was observed with high Pearson correlation coefficients >0.95 between replicate enrichments and median CV of 20%. The combined sensitivity of microflow HPLC with Zeno SWATH allows for the high-throughput quantitation of an extensive proteome and shallow phosphoproteome from small tissue samples.

High-Throughput Glycan Profiling of Human Serum IgG Subclasses Using Parallel Reaction Monitoring Peptide Bond Fragmentation of Glycopeptides and Microflow LC-MS.

LC-MS-based N-glycosylation profiling in four human serum IgG subclasses (IgG1, IgG2, IgG3, and IgG4) often requires additional affinity-based enrichment of specific IgG subclasses, owing to the high amino acid sequence similarity of Fc glycopeptides among subclasses. Notably, for IgG4 and the major allotype of IgG3, the glycopeptide precursors share identical retention time and mass and therefore cannot be distinguished based on precursor or glycan fragmentation. Here, we developed a parallel reaction monitoring (PRM)-based method for quantifying Fc glycopeptides through combined transitions generated from both glycosidic and peptide bond fragmentation. The latter enables the subpopulation of IgG3 and IgG4 to be directly distinguished according to mass differences without requiring further enrichment of specific IgG subclasses. In addition, a multinozzle electrospray emitter coupled to a capillary flow liquid chromatograph was used to increase the robustness and detection sensitivity of the method for low-yield peptide backbone fragment ions. The gradient was optimized to decrease the overall run time and make the method compatible with high-throughput analysis. We demonstrated that this method can be used to effectively monitor the relative levels of 13 representative glycoforms, with a good limit of detection for individual IgG subclasses.

Nontargeted Plasma Proteomic Analysis of Renal Disease and Pulmonary Hypertension in Patients with Sickle Cell Disease.

Sickle cell disease (SCD) is characterized by red blood cell sickling, vaso-occlusion, hemolytic anemia, damage to multiple organ systems, and, as a result, shortened life expectancy. Sickle cell disease nephropathy (SCDN) and pulmonary hypertension (pHTN) are common and frequently co-occurring complications of SCD; both are associated with markedly accelerated mortality. To identify candidate circulating biomarkers of SCDN and pHTN, we used mass spectrometry to quantify the relative abundance of >1000 proteins in plasma samples from 189 adults with SCD from the Outcome Modifying Genes in SCD (OMG-SCD) cohort (ProteomeXchange identifier PXD048716). Forty-four proteins were differentially abundant in SCDN, most significantly cystatin-C and collagen α-1(XVIII) chain (COIA1), and 55 proteins were dysregulated in patients with SCDN and pHTN, most significantly insulin-like growth factor-binding protein 6 (IBP6). Network analysis identified a module of 133 coregulated proteins significantly associated with SCDN, that was enriched for extracellular matrix proteins, insulin-like growth factor binding proteins, cell adhesion proteins, EGF-like calcium binding proteins, and several cadherin family members. Collectively, these data provide a comprehensive understanding of plasma protein changes in SCDN and pHTN which validate numerous studies of chronic kidney disease and suggest shared profiles of protein disruption in kidney dysfunction and pHTN among SCD patients.

Advances in Microflow Cytometry-Based Molecular Detection Methods for Improved Future MDS Cancer Diagnosis.

Myelodysplastic syndromes (MDS) are a rare form of early-stage blood cancer that typically leads to leukemia and other deadly complications. The typical diagnosis for MDS involves a mixture of blood tests, a bone marrow biopsy, and genetic analysis. Flow cytometry has commonly been used to analyze these types of samples, yet there still seems to be room for advancement in several areas, such as the limit of detection, turnaround time, and cost. This paper explores recent advancements in microflow cytometry technology and how it may be used to supplement conventional methods of diagnosing blood cancers, such as MDS and leukemia, through flow cytometry. Microflow cytometry, a more recent adaptation of the well-researched and conventional flow cytometry techniques, integrated with microfluidics, demonstrates significant potential in addressing many of the shortcomings flow cytometry faces when diagnosing a blood-related disease such as MDS. The benefits that this platform brings, such as portability, processing speed, and operating cost, exemplify the importance of exploring microflow cytometry as a point-of-care (POC) diagnostic device for MDS and other forms of blood cancer.

A Novel Bubble-based Microreactor for Enhanced Mass Transfer Dynamics toward Efficient Electrocatalytic Nitrogen Reduction.

Electrocatalytic nitrogen reduction reaction (eNRR) is a promising method for sustainable ammonia production. Although the majority of studies on the eNRR are devoted to developing efficient electrocatalysts, it is critical to study the influence of mass transfer because of the poor N2 transfer efficiency. Herein, a novel bubble-based microreactor (BBMR) is proposed that efficiently promotes the mass transfer behavior during the eNRR using microfluidic strategies. The BBMR possesses abundant triphasic interfaces and provides spatial confinement and accurate potential control, ensuring rapid mass transfer dynamics and improved eNRR performance, as confirmed by experimental and simulation studies. The ammonia yield of the reaction over Ag nanoparticles can be enhanced to 31.35 µg h-1 mgcat. -1, which is twice that of the H-cell. Excellent improvements are also achieved using Ru/C and Fe/g-CN catalysts, with 5.0 and 8.5 times increase in ammonia yield, respectively. This work further demonstrates the significant effect of mass transfer on the eNRR performance and provides an effective strategy for process enhancement through electrode design.

Computational Hyperspectral Microflow Cytometry.

Miniaturized flow cytometry has significant potential for portable applications, such as cell-based diagnostics and the monitoring of therapeutic cell manufacturing, however, the performance of current techniques is often limited by the inability to resolve spectrally-overlapping fluorescence labels. Here, the study presents a computational hyperspectral microflow cytometer (CHC) that enables accurate discrimination of spectrally-overlapping fluorophores labeling single cells. CHC employs a dispersive optical element and an optimization algorithm to detect the full fluorescence emission spectrum from flowing cells, with a high spectral resolution of ≈3 nm in the range from 450 to 650 nm. CHC also includes a dedicated microfluidic device that ensures in-focus imaging through viscoelastic sheathless focusing, thereby enhancing the accuracy and reliability of microflow cytometry analysis. The potential of CHC for analyzing T lymphocyte subpopulations and monitoring changes in cell composition during T cell expansion is demonstrated. Overall, CHC represents a major breakthrough in microflow cytometry and can facilitate its use for immune cell monitoring.

Vacuum Insulated Probe Heated Electrospray Ionization Source Enhances Microflow Rate Chromatography Signals in the Bruker timsTOF Mass Spectrometer.

By far the largest contribution to ion detectability in liquid chromatography-driven mass spectrometry-based proteomics is the efficient generation of peptide molecular ions by the electrospray source. To maximize the transfer of peptides from the liquid to gaseous phase and allow molecular ions to enter the mass spectrometer at microspray flow rates, an efficient electrospray process is required. Here we describe the superior performance of newly design vacuum insulated probe heated electrospray ionization (VIP-HESI) source coupled to a Bruker timsTOF PRO mass spectrometer operated in microspray mode. VIP-HESI significantly improves chromatography signals in comparison to electrospray ionization (ESI) and nanospray ionization using the captivespray (CS) source and provides increased protein detection with higher quantitative precision, enhancing reproducibility of sample injection amounts. Protein quantitation of human K562 lymphoblast samples displayed excellent chromatographic retention time reproducibility (<10% coefficient of variation (CV)) with no signal degradation over extended periods of time, and a mouse plasma proteome analysis identified 12% more plasma protein groups allowing large-scale analysis to proceed with confidence (1,267 proteins at 0.4% CV). We show that the Slice-PASEF VIP-HESI mode is sensitive in identifying low amounts of peptide without losing quantitative precision. We demonstrate that VIP-HESI coupled with microflow rate chromatography achieves a higher depth of coverage and run-to-run reproducibility for a broad range of proteomic applications. Data and spectral libraries are available via ProteomeXchange (PXD040497).

Antibody titrations are critical for microflow cytometric analysis of extracellular vesicles.

Optimization of flow cytometry assays for extracellular vesicles (EVs) often fail to include appropriate reagent titrations - the most critically antibody titration is either not performed or is incomplete. Using nonoptimal antibody concentration is one of the main sources of error leading to a lack of reproducible data. Antibody titration for the analysis of antigens on the surface of EVs is challenging for a variety of technical reasons. Using platelets as surrogates for cells and platelet-derived particles as surrogates for EV populations, we demonstrate our process for antibody titration, highlighting some of the key analysis parameters that may confound and surprise new researchers moving into the field of EV research. Additional care must be exercised to ensure instrument and reagent controls are utilized appropriately. Complete graphical analysis of positive and negative signal intensities, concentration, and separation or stain index data is highly beneficial when paired with visual analysis of the cytometry data. Using analytical flow cytometry procedures optimized for cells for EV analysis can lead to misleading and nonreproducible results.

Combination of automated sample preparation and micro-flow LC-MS for high-throughput plasma proteomics.

BACKGROUND: Non-invasive detection of blood-based markers is a critical clinical need. Plasma has become the main sample type for clinical proteomics research because it is easy to obtain and contains measurable protein biomarkers that can reveal disease-related physiological and pathological changes. Many efforts have been made to improve the depth of its identification, while there is an increasing need to improve the throughput and reproducibility of plasma proteomics analysis in order to adapt to the clinical large-scale sample analysis. METHODS: We have developed and optimized a robust plasma analysis workflow that combines an automated sample preparation platform with a micro-flow LC-MS-based detection method. The stability and reproducibility of the workflow were systematically evaluated and the workflow was applied to a proof-of-concept plasma proteome study of 30 colon cancer patients from three age groups. RESULTS: This workflow can analyze dozens of samples simultaneously with high reproducibility. Without protein depletion and prefractionation, more than 300 protein groups can be identified in a single analysis with micro-flow LC-MS system on a Orbitrap Exploris 240 mass spectrometer, including quantification of 35 FDA approved disease markers. The quantitative precision of the entire workflow was acceptable with median CV of 9%. The preliminary proteomic analysis of colon cancer plasma from different age groups could be well separated with identification of potential colon cancer-related biomarkers. CONCLUSIONS: This workflow is suitable for the analysis of large-scale clinical plasma samples with its simple and time-saving operation, and the results demonstrate the feasibility of discovering significantly changed plasma proteins and distinguishing different patient groups.

Size-resolved counting of circulating tumor cells on pinched flow-based microfluidic cytometry.

Precise cell detecting and counting is meaningful in circulating tumor cells (CTCs) analysis. In this work, a simple cyclic olefin copolymer (COC) microflow cytometer device was developed for size-resolved CTCs counting. The proposed device is constructed by a counting channel and a pinched injection unit having three channels. Through injection flow rate control, microspheres/cells can be focused into the centerline of the counting channel. Polystyrene microspheres of 3, 9, 15, and 20 µm were used for the microspheres focusing characterization. After coupling to laser-induced fluorescence detection technique, the proposed device was used for polystyrene microspheres counting and sizing. A count accuracy up to 97.6% was obtained for microspheres. Moreover, the proposed microflow cytometer was applied to CTCs detecting and counting. To mimic blood sample containing CTCs and CTCs mixture with different subtypes, an MDA-MB-231 (human breast cell line) spiked red blood cells sample and a mixture of MDA-MB-231 and MCF-7 (human breast cell line) sample were prepared, respectively, and then analyzed by the developed pinched flow-based microfluidic cytometry. The simple fabricated and easy operating COC microflow cytometer exhibits the potential in the point-of-care clinical application.

Deep learning image analysis models pretrained on daily objects are useful for the preliminary characterization of particulate pharmaceutical samples.

Visible and subvisible particles are a quality attribute in sterile pharmaceutical samples. A common method for characterizing and quantifying pharmaceutical samples containing particulates is imaging many individual particles using high-throughput instrumentation and analyzing the populations data. The analysis includes conventional metrics such as the particle size distribution but can be more sophisticated by interpreting other visual/morphological features. To avoid the hurdles of building new image analysis models capable of extracting such relevant features from scratch, we propose using well-established pretrained deep learning image analysis models such as EfficientNet. We demonstrate that such models are useful as a prescreening tool for high-level characterization of biopharmaceutical particle image data. We show that although these models are originally trained for completely different tasks (such as the classification of daily objects in the ImageNet database), the visual feature vectors extracted by such models can be useful for studying different types of subvisible particles. This applicability is illustrated through multiple case studies: (i) particle risk assessment in prefilled syringe formulations containing different particle types such as silicone oil, (ii) method comparability with the example of accelerated forced degradation, and (iii) excipient influence on particle morphology with the example of Polysorbate 80 (PS80). As examples of agnostic applicability of pretrained models, we also elucidate the application to two high-throughput microscopy methods: microflow and background membrane imaging. We show that different particle populations with different morphological and visual features can be identified in different samples by leveraging out-of-the-box pretrained models to analyze images from each sample.

Current State of Microflow Trifluoromethylation Reactions.

The trifluoromethyl group is a powerful structural motif in drugs and polymers; thus, developing trifluoromethylation reactions is an important area of research in organic chemistry. Over the past few decades, significant progress has been made in developing new methods for the trifluoromethylation of organic molecules, ranging from nucleophilic and electrophilic approaches to transition-metal catalysis, photocatalysis, and electrolytic reactions. While these reactions were initially developed in batch systems, more recent microflow versions are highly attractive for industrial applications owing to their scalability, safety, and time efficiency. In this review, we discuss the current state of microflow trifluoromethylation. Approaches for microflow trifluoromethylation based on different trifluoromethylation reagents are described, including continuous flow, flow photochemical, microfluidic electrochemical reactions, and large-scale microflow reactions.

Sensitivity and Uncertainty Analysis of Micro-Flow Imaging for Sub-Visible Particle Measurements Using Artificial Neural Network.

PURPOSE: During biopharmaceutical drug manufacturing, storage, and distribution, proteins in both liquid and solid dosage forms go through various processes that could lead to protein aggregation. The extent of aggregation in the sub-micron range can be measured by analyzing a liquid or post-reconstituted powder sample using Micro-Flow Imaging (MFI) technique. MFI is widely used in biopharmaceutical industries due to its high sensitivity in detecting and analyzing particle size distribution. However, the MFI's sensitivity to various factors makes accurate measurement challenging. Therefore, in light of the inherent variability of the method, this work aims to explore the capabilities of an adopted coupled sensitivity analysis and machine learning algorithm to quantify the influencing factors on the formed sub-visible particles and method variability. METHODS: The proposed algorithm consists of two interconnected components, namely a surrogate model with a neural network and a sensitivity analyzer. A machine learning tool based on artificial neural networks (ANN) is constructed with MFI data. The best fit with an optimized configuration is found. Sensitivity and uncertainty analysis is performed using this network as the surrogate model to understand the impacts of input parameters on MFI data. RESULTS: Results reveal the most impactful reconstitution preparation factors and others that are masked by the instrument variabilities. It is shown that instrument inaccuracy is a function of size category, with higher variabilities associated with larger size ranges. CONCLUSION: Utilizing this tool while assessing the sensitivity of outputs to various parameters, measurement variabilities for analytical characterization tests can be quantified.

Micro- and macrocirculatory effects of norepinephrine on anaesthesia-induced hypotension: a prospective preliminary study.

BACKGROUND: Intraoperative arterial hypotension (IOH) leads to increased postoperative morbidity. Norepinephrine is often use to treat IOH. The question regarding the mode of administration in either a bolus or continuous infusion remains unanswered. The aim of the present study was to describe and compare the effects on macrocirculation and microcirculation of a bolus and a continuous infusion of norepinephrine to treat IOH. METHODS: We conducted a prospective observational study with adult patients who underwent neurosurgery. Patients with invasive arterial blood pressure and cardiac output (CO) monitoring were screened for inclusion. All patients underwent microcirculation monitoring by video-capillaroscopy, laser doppler, near-infrared spectroscopy technology, and tissular CO2. In case of IOH, the patient could receive either a bolus of 10 µg or a continuous infusion of 200 µg/h of norepinephrine. Time analysis for comparison between bolus and continuous infusion were at peak of MAP. The primary outcome was MFI by videocapillaroscopy. RESULTS: Thirty-five patients were included, with 41 boluses and 33 continuous infusion. Bolus and continuous infusion induced an maximal increase in mean arterial pressure of +30[20-45] and +23[12-34] %, respectively (P=0,07). For macrocirculatory parameters, continuous infusion was associated with a smaller decrease in CO and stroke volume (p<0.05). For microcirculatory parameters, microvascular flow index (-0,1 vs. + 0,3, p=0,03), perfusion index (-12 vs. +12%, p=0,008), total vessel density (-0,2 vs. +2,3 mm2/mm2, p=0,002), showed significant opposite variations with bolus and continuous infusion, respectively. CONCLUSIONS: These results on macro and microcirculation enlighten the potential benefits of a continuous infusion of norepinephrine rather than a bolus to treat anaesthesia-induced hypotension. TRIAL REGISTRATION: (NOR-PHARM: 1-17-42 Clinical Trials: NCT03454204), 05/03/2018.

The Preliminary Application of Simultaneous Display of Contrast-Enhanced Ultrasound and Micro-Flow Imaging Technology in the Diagnosis of Hepatic Tumors.

OBJECTIVES: To evaluate the value of simultaneous display of contrast-enhanced ultrasound and micro-flow imaging technology (CEUS-MFI) in intra-tumoral vessel detection and hepatic tumor diagnosis. METHODS: A total of 82 patients with 82 focal liver lesions were enrolled in this study. Each patient received ultrasound exams including color Doppler flow imaging (CDFI), micro-flow imaging (MFI), contrast-enhanced ultrasound (CEUS), and CEUS-MFI with a Philips EPIQ7 ultrasound imaging system. The intra-tumoral vessels detected by CDFI, MFI, and CEUS-MFI were compared, respectively. The accuracy and confidence of using CEUS and CEUS-MFI in diagnosing hepatic tumors were also compared. RESULTS: CEUS-MFI was capable of detecting more hepatic intra-tumoral vessels than MFI (P = .000) and CDFI (P = .000). Compared with CEUS, CEUS-MFI improved the diagnostic accuracy of hepatic lesions (P = .009). Particularly, among the correctly diagnosed hepatic lesions, the number of cases where radiologists diagnosed with great confidence was increased from 88.4% (61/69) with CEUS only to 92.4% (73/79) with CEUS-MFI (P = .041). CONCLUSIONS: CEUS-MFI is sensitive in detecting hepatic intra-tumoral vessels and can improve the accuracy and confidence of radiologists in diagnosing hepatic lesions.

Improving the performance and versatility of microfluidic thread electroanalytical devices by automated injection with electronic pipettes: a new and powerful 3D-printed analytical platform.

Microfluidic cotton thread-based electroanalytical devices (µTEDs) are analytical systems with attractive features such as spontaneous passive flow, low cost, minimal waste production, and good sensitivity. Currently, sample injection in µTEDs is performed by hand using manual micropipettes, which have drawbacks such as inconstant speed and position, dependence of skilled analysts, and need of physical effort of operator during prolonged times, leading to poor reproducibility and risk of strain injury. As an alternative to these inconveniences, we propose, for the first time, the use of electronic micropipettes to carry out automated injections in µTEDs. This new approach avoids all disadvantages of manual injections, while also improving the performance, experience, and versatility of µTEDs. The platform developed here is composed by three 3D-printed electrodes (detector) attached to a 3D-printed platform containing an adjustable holder that keeps the electronic pipette in the same x/y/z position. As a proof-of-concept, both injection modes (manual and electronic) were compared using three model analytes (nitrite, paracetamol, and 5-hydroxytryptophan) on µTED with amperometric detection. As result, improved analytical performance (limits of detection between 2.5- and 5-fold lower) was obtained when using electronic injections, as well as better repeatability/reproducibility and higher analytical frequencies. In addition, the determination of paracetamol in urine samples suggested better precision and accuracy for automated injection. Thus, electronic injection is a great advance and changes the state-of-art of µTEDs, mainly considering the use of more modern and versatile electronic pipettes (wider range of pre-programmed modes), which can lead to the development of even more automated systems.

Investigation on the Effective Measures for Improving the Performance of Calorimetric Microflow Sensor.

The performance of the calorimetric microflow sensor is closely related to the thermal sensing part design, including structure parameter, heater temperature, and operation environment. In this paper, several measures to enhance the performance of the calorimetric microflow sensor were proposed and further verified by numerical simulations. The results demonstrate that it is more favorable to reduce the negative impact of flow separation as the space between detectors and heater is set to be 1.6 µm so as to improve the accuracy of the sensor. With an appropriate gap, the front arranged obstacle of the upstream detector can effectively widen the measure range of the sensor, benefiting from the decrease in upstream viscous dissipation. Compared to a cantilever structure, the resonances can be effectively suppressed when the heater and detectors are designed as bridge structures. In particular, the maximum amplitude of the bridge structure is only 0.022 µm at 70 sccm, which is 53% lower than that of the cantilever structure. The optimized sensor widens the range by 14.3% and significantly increases the sensitivity at high flow rates. Moreover, the feasibility of the improved measures is also illustrated via the consistency of the trend between the simulation results and experimental ones.