GaN has wide bandgap, high critical electric field, and high electron saturation velocity, making it an ideal candidate for power switching electronics. However, the development of GaN-based power electronics is still hampered by the high cost of GaN substrate and selective area doping capabilities (especially for p-type) to produce laterally patterned p-n junctions. Epitaxial lateral overgrowth (ELO) is known to render low-dislocation-density GaN in the overgrown regions (wings) on inexpensive foreign substrates. Furthermore, the combination of ELO (prior to the coalescence stage) and in-situ doping process produces the half-core-shell doping profile which has been used in the optoelectronics such as microrod LEDs. However, the application of ELO-GaN has not been explored in the power applications mostly because such doping profile is not configured to withstand high reverse blocking voltage unless the modified structures and methods could be adopted. In this dissertation, a holistic approach was employed to study the innovative measures either in the material growth or device processing stage to tailor the half-core-shell doping profile produced by the ELO of GaN into the desired selective-area doping profiles for power switching electronics featuring the building block of laterally patterned p-n junctions. For the device processing innovation, the concept of true-lateral device architecture was proposed which consists of fully lateral aligned p-n junctions. The general advantages of such device architecture were comprehensively discussed from various aspects. In addition, the low-dislocation density GaN in the wing regions of ELO was fully utilized as an ideal drift layer of a power device. As a result, both power diodes (Schottky barrier diode and p-n junction diode) and power bipolar transistors (gated lateral power bipolar junction transistor and insulated gate bipolar transistor) with the true-lateral device architecture were experimentally demonstrated either with superior performance or for the first time, highlighted by the record high critical electric field in a GaN p-n junction and the record high current gain of a power bipolar transistor. Alternatively, for the material growth innovation, the hybrid epitaxy-enabled substrate transfer approach was demonstrated to produce the GaN substrate with repeating laterally patterned p-n junctions suitable for a number of advanced electronic devices such as a planar-gate vertical MOSFET. In addition, the selective area doping profiles of the GaN substrate product also rendered a number of state-of-the-art characterization techniques which provided valuable information to study the incorporation and diffusion of dopants (especially for acceptors) in GaN. With these innovative measures and a deeper understanding of selective area doping of GaN, the potential of epitaxial lateral overgrowth to simultaneously realize the low threading dislocation density and the selective-area doping profile (lateral patterned p-n junctions) was initially and finally unleashed to yield unprecedented opportunities in the power electronic applications. This may spawn a revival of interest into ELO-GaN for power electronic applications.

Surface plasmons (SPs) are coherent delocalized electron oscillations that exist at the metal-dielectric interface. The charge motion in the surface plasmon creates intense electromagnetic fields at certain locations of the interface, which are referred to as "hot spots". The intense electromagnetic field associated with the excitation of surface plasmons has found applications in various bio-sensing techniques.

A unique plasmonic hybrid platform, graphene-Au pyramid structure, was invented by our group. The hybrid platform provides an ultra intense surface plasmon field and has a bio-compatible surface, making it a powerful tool for bio-sensing. The work to refine the nanostructure for generation of stronger surface plasmon field has been intensively explored by my group mates. Aside from generation of stronger surface plasmon fields, this research was done to improve several surface plasmon based techniques through the hybrid platform and to explore their applications in the label free bio-sensing field. The ultimate goal of my thesis work is to develop an integrated system for biological detection and analysis with high sensitivity and specificity.

In the bio-sensing field, especially in regards to remote detection in an analyte, the biological entities (e.g. biomolecules and cells) of interest are always dispersed in the solution. This would be a concern if we were using plasmon resonance powered techniques by themselves. The first part of this research focuses on Plasmonic tweezers, a manipulation technique to attract and capture biological entities onto the plasmonics surface. Plasmonic tweezers is noninvasive manipulation technique, in which a near-field gradient force is generated by the surface plasmon field around hot spots. It can be used to precisely control the position of biological entities. However, the near-field property of plasmonic tweezer limits its functional range in capturing biological samples. To remedy this problem, electrostatic bias is used in conjunction with plasmonic tweezers. Electrostatic bias produces a long range force whose effects spread across the entire space between a pair of electrodes. It compensates for the plasmonic tweezers’ short range limitation by “condensing” molecules throughout the analyte to a layer immediately adjacent to the plasmonic surface. By using plasmonic tweezers and an electrostatic force together, the biological entities can be confined to sub-wavelength dimensions near the hot spots. For the plasmonics based bio-detection methods, the hot spots are always where the signal generates from. Therefore, this self-aligned trapping method is used to effectively increase the sensitivity and selectivity of bio-sensing techniques.

After the biological entities are attracted to the plasmonics surface, the next step is to develop specific detection techniques. The second part of this thesis is to demonstrate the capability of the surface-enhanced Raman spectroscopy (SERS) techniques based on a nanopyramid array hybrid platform. SERS is a surface-sensitive technique employing strong plasmon resonance fields to enhance the Raman signal by several orders of magnitude. This enables even the detection of single molecules. Also, compared with other nanostructures, the hybrid platform has distinct advantages. The nanopyramid array is an open structure, in which hot spots are located between neighboring pyramids. As such, large biomolecules and biological entities can easily move into these regions and their Raman signals can be enhanced. By introducing single layer graphene, the hybrid platform provides a bio-friendly surface, preventing biological entities from being affected by toxic metals such as silver. The signal of the graphene layer can also serve as a built-in gauge of local electromagnetic field intensity used to indicate the distribution of the hot spots. These properties make the hybrid platform a powerful tool for biological detection and analysis. In this thesis work, the hybrid platform is employed to generate SERS signals of biological samples, in particular, the characterization of exosomes and T-cells. However, the biological entities would naturally bring variations to the SERS signals due variations in the type and quantity of their chemical compositions. In our research, principle component analysis (PCA) is also employed to interpret the SERS data and provide a statistical investigation of the biological samples.

To achieve dynamic monitoring of biological processes, higher sensitivity and temporal resolution are preferred. In the last part of this thesis, another label free bio-sensing technique, surface-enhanced coherent anti-Stokes Raman spectroscopy (SECARS), was developed based on the hybrid platform. A multiplicative enhancement of the Raman signal over CARS and SERS is achieved using a novel SECARS setup. Compared to previous setups, it shows a broadband feature with high spectral resolution, which is preferred in biological detection. The novel setup based on the hybrid platform could be a powerful tool for not only the characterization of biological entities but also the dynamic monitoring of various biological processes.

H2 has long been known to be one of the highest energy density fuels. Fuel cell technology has been actively pursued as an environmentally friendly power source for automobiles. The promise of the technology is hindered by the lacking of a sustainable way of producing H2. Solar water-splitting via photo-electrochemical cells (PEC) is the most promising approach which converts the sustainable solar energy to the chemical energy store inside the H2 bonding. The materials that can be used in a PEC cell must fulfil a variety of thermodynamic and kinetic requirements to ensure good efficiency and durability. Single-layer MoS2 possess the corrected energy bandgap of 1.9 eV which allows for sufficient over-potential while still being capable of absorbing the majority of the solar spectrum. However, the limited optical absorbance from single-layer MoS2 prevent it from widely used. This shortcoming of the single-layer thickness of MoS2 is overcome by superimposing the MoS2 with plasmonic surface that serves to amplify the enhanced electromagnetic field where the MoS2 is located, allowing single-layer MoS2 to efficiently absorb Sun light thus producing H2.

In the first part of dissertation, a novel two-step chemical vapor deposition method is developed to consistently grow high coverage and exclusive single-layer MoS2. Up to 90% surface coverage and single-layer MoS2 is successfully fabricated. This is a crucial step to conduct the follow-up experiments.

In the second part of dissertation, A facile one-pot synthetic approach for synthesis hollow Au nanoframes structure is reported for the first time. A growth mechanism has been revealed that involves a synergistic function of Ag and Br ions. The presence of Ag+ lead to observed self-limiting of Au film thickness whereas Au {111} facets are preferentially attacked by the presence of Br- in the reaction ambient. Combined simulation and experimental studies show strong plasmonic effect that the hybrid platform made of graphene/Au nanoframes is capable of detecting analytes at concentration levels down to 10-9 M by using the surface-enhanced Raman spectroscopy (SERS) technique.

In the last part of the dissertation, the plasmonic effect generated from nano-structured metal surfaces is introduced to offset the small thickness from single-layer MoS2 and improve the overall absorption. It is demonstrated for the first time that using single-layer MoS2 as a well-defined nanospacer between Au-nanoparticles and Au-film (gap plasmon system). The field enhancement is known to be inversely proportional to this gap thickness. Hence reducing the gap thickness is important to achieve the highest possible field enhancement. �In this work, it is demonstrated for the first time that using single-layer MoS2 as a well-defined nanospacer between Au-nanoparticles (AuNPs) and Au-film, which could offer an extremely high localized electric field enhancement within the gap. The MoS2 Raman intensity with the Surface Enhanced Raman Scattering (SERS) enhancement factor (EF) up to 5x106 is obtained from the MoS2-Au gap plasmon system. A 5-fold increase in the photocurrent is obtained from the MoS�2-Au gap plasmon as the working electrode compared to that from bare MoS2 prepared under the same condition. Compared with individual metal nanoparticles commonly used to enhance thin-film photocatalytic process, gap-plasmon could theoretically produce 8 orders of magnitude higher SERS EF and precise control the hot spot location to superimpose where ultrathin materials locate thus using the higher incident energy available.

Surface Enhanced Raman Spectroscopy (SERS) is a surface-sensitive technique based on plasmon resonance, which can enhance Raman signal intensity by several orders of magnitude, enabling even detection of a single molecule. Material topics involving SERS contain manipulation of specific hot spots through nano-fabrication and enhancement of substrate sensitivity and affinity to interested molecules. The main objective of this research is to gain fundamental understanding of capability limit of SERS optical fiber technology through systematic experimental studies combined with simulations.

SERS fiber probe based on Au nanotriangle has been fabricated and factors affecting Raman signal through fiber are investigated. These factors include numerical aperture of objective lens, slit width of spectrometer, fiber length and SERS array size on fiber facet. Size of SERS array coated on fiber facet is one primary factor accounting for Raman signal loss, which is ultimately due to decrease in laser power density on the fiber end. The most fundamental optimization consideration for SERS fiber probe is a tradeoff between sensitivity and spatial resolution.

A novel Au pyramid/ring SERS platform with enhancement of spatial confinement and electric field is demonstrated for potential SERS fiber probe application through finite-difference time-domain (FDTD) simulation. The FDTD simulation work provides an alternative way to enhance Raman signal intensity while keep high spatial resolution for SERS fiber probe. Excitation of surface plasmon polaritons (SPPs) by circular Au rings and coupling of SPPs with Au pyramids are studied to improve electric field and overall SERS enhancement of the platform. Au pyramids combined with rings can achieve confinement of the electric field within a 700?700 nm region, which could provide higher spatial resolution for SERS fiber probe.

In addition, a practical way of patterning metallic nanostructures for achieving high SERS enhancement factors (EFs) and high hot spot density is demonstrated. By superimposing one layer Au triangle array on another to form multilayer triangle array, SERS signal can be enhanced by two orders of magnitude. The physical understanding of the SERS enhancement of multilayer triangle array is also developed through FDTD simulation. The drastic increased SERS EFs and hot spot density are due to an increase of amount of gaps formed between Au triangles and a decrease of the gap size.

The thesis also presents a serendipitous discovery of bilayer MoS2 spectral features when exploring MoS2 as a potential SERS enhancement substrate. CVD grown AA’ (60? stacking) and AB (0? stacking) stacked bilayer MoS2 have distinct properties in terms of resonance Raman and photoluminescence. Raman and photoluminescence spectra of AA’ and AB stacked bilayer MoS2 are obtained on Au nano-pyramid surfaces under strong plasmon resonance. A Raman peak named as “a” peak appears in AA’ stacked bilayer MoS2 resonance Raman spectra but not in AB stacked bilayer, a feature that can be used as Raman finger print to differentiate the two types of bilayer MoS2.

This dissertation presents a novel suspended graphene ribbon device for electrostatic discharge (ESD) applications. The device structure is proposed and fabricated after careful design considerations. Compared to the conventional ESD devices such as diodes, bipolar junction transistors (BJTs), and metal-oxide-semiconductor field-effect transistors (MOSFETs), the proposed device structure is believed to render several advantages including zero leakage, low parasitic effects, fast response, and high current carrying capability, etc. A process flow is developed for higher yield and reliability of the suspended graphene ribbon device which is very delicate in nature. Direct current (DC) and transmission-line pulse test (TLP) measurements are carried out to investigate the switch-on behavior of the device which is crucial for ESD protection. DC measurement with a different configuration is used to characterize the mechanical shape evolution of the graphene ribbon upon biasing. Finite Element Simulations are also conducted to verify the experimental results, which are in good agreements. Furthermore, the breakdown properties of graphene ribbons are tested using TLP. It is found that graphene has a better current drivability compared to copper wires which is widely used as interconnects in integrated circuits (ICs). Also, bi-layer graphene has a higher breakdown current than monolayer graphene which indicates that multilayer graphene should be superior in current discharging. Last, Ab inito calculations are carried out to study the growth mechanism of multilayer graphene which is needed for graphene homo-epitaxy with precise control. It is found that a carbon cluster with six carbon atoms has the smallest kinetic barrier thus largest surface diffusivity during surface diffusion. So it is believed to be the most favorable diffusing species for graphene homo-epitaxy.

Surface Enhanced Raman Scattering (SERS) has attracted explosive interest for the wealth of vibrational information it provides with minimal invasive effects to target analyte. Nanotechnology, especially in the form of noble metal nanoparticles exhibit unique electromagnetic and chemical characteristics that are explored to realize ultra-sensitive SERS detection in chemical and biological analysis. Graphene, atom-thick carbon monolayer, exhibits superior chemical stability and bio-compatibility. A combination of SERS-active metal nanostructures and graphene will create various synergies in SERS.

The main objective of this research was to exploit the applications of the graphene-Au tip hybrid platform in SERS. The hybrid platform consists of a periodic Au nano-pyramid substrate to provide reproducible plasmonic enhancement, and the superimposed monolayer graphene sheet, serving as “built-in” Raman marker. Extensive theoretical and experimental studies were conducted to determine the potentials of the hybrid platform as SERS substrate. Results from both Finite-Domain Time-Domain (FDTD) numerical simulation and Raman scattering of graphene suggested that the hybrid platform boosted a high density of hotspots yielding 1000 times SERS enhancement of graphene bands.

Ultra-high sensitivity of the hybrid platform was demonstrated by bio-molecules including dye, protein and neurotransmitters. Dopamine and serotonin can be detected and distinguished at 10-9 M concentration in the presence of human body fluid. Single molecule detection was obtained using a bi-analyte technique. Graphene supported a vibration mode dependent SERS chemical enhancement of ~10 to the analyte.

Quantitative evaluation of hotspots was presented using spatially resolved Raman mapping of graphene SERS enhancement. Graphene plays a crucial role in quantifying SERS hotspots and paves the path for defining SERS EF that could be universally applied to various SERS systems. A reproducible and statistically reliable SERS quantification approach using the hybrid platform was proposed. The SERS mapping based approach not only leverages the ultra-sensitivity but also minimizes the spot-to-spot variations.

Feasibility of biomedical diagnosis with the hybrid platform was exploited by colon cancer cell sensing and time-dependent SERS of amyloid β protein monomer. The capabilities of the platform are demonstrated by colon cancer cell detection in simulated body fluid background with cell concentration down to 50 cells /mL. Sensitivity of 95% was evidenced by Principle Components Analysis (PCA). Besides, a noticeable evolution profile of the Aβ SERS peaks was observed and attributed to the Aβ configurational change. Taken together, the results suggested the graphene-plasmonic hybrid platform can potentially deliver a biomedical detection and diagnostic imaging platform with superior sensitivity and resolution.

Graphene is a 2D sheet of carbon atoms arranged in a hexagonal lattice whose unique structure gives rise to extraordinary mechanical and electronic properties. For these reasons, suspended graphene structures have potential applications in micro-electromechanical systems (MEMS). This dissertation serves as an exposition exploring the electromechanical response of suspended graphene and its applications in MEMS. After a thorough introduction to graphene is provided, the dissertation presents a series of studies. First, a suspended graphene ribbon device was studied as a mechanical switch used to provide electrostatic discharge protection tosemiconductor integrated circuits, establishing proof of concept via standard electrostatic discharge industry testing. Second, the effect of polymer residue from device fabrication processes on the electromechanical response of suspended graphene was investigated. After rigorous study using innovative compliant mechanisms, polymer residue was found to have two prominent effects on suspended graphene: 1) a variation polymer residue thickness led to a variation in end device parameters such as the pull-in voltage and 2) the polymer residue itself supports the suspension of graphene by increasing the rigidity of the suspended structure. These results have implications to the manufacturability and reliability of suspended graphene MEMS devices. Finally, a theoretical study modeling the suspended graphene ribbon device was conducted for a separate application: a resonator for high frequency power conversion, an idea that was made attractive again by the extraordinary material properties of graphene. In this study, the device design, electromechanical modeling, and results relevant to high frequency power conversion applications are presented and discussed. The results indicate that a suspended graphene ribbon resonator can generate oscillation frequencies within the THz gap when the graphene sheet itself approaches sufficiently small dimensions; however, there also exists a tradeoff between the power generated and the signal quality which must be taken into account when designing these resonators. In all the studies presented, the device design, experimental procedures, algorithms for computer modeling, etc. are explained in detail. Lastly, standard semiconductor industry fabrication and characterization techniques were primarily used in doing this work, imbuing the potential for industrial large-scale adoption in the future.

Early diagnosis of Alzheimer’s disease (AD) is critical for disease prevention and cure, but no method to do so has yet been developed that had the required sensitivity and specificity. Computational methods are increasingly being applied in these efforts. One such method is "deep learning." We propose here a convolutional neural network-based AD diagnosis approach using surface enhanced Raman spectroscopy (SERS) fingerprints of human cerebrospinal fluid (CSF). To realize the testing, we further prove the reliability of a SERS hybrid platform from its quantification capability and orientation dependence. Analysis using Amyloid beta (Aβ) to prove the biological feasibility and test the specificity of the platform is also done.

We report results demonstrating the reproducibility and accuracy of this novel SERS data analysis platform. We have achieved 100% reproducibility in double blind experiments and 92% accuracy in disease diagnosis. Comparison of the SERS-neural network approach with single biomarker tests shows it is more accurate, thus it may have substantial value in the differential diagnosis of AD as well as other neurodegenerative disorders.

We also show here that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) readily distinguishes small biological differences: Aβ40 and Aβ42. We show further, through comparison of assembly-dependent changes in secondary structure and morphology, that the SERS/PCA approach readily and unambiguously differentiates closely related assembly stages not readily differentiable by circular dichroism spectroscopy, electron microscopy, or other techniques.

To test the substrate feature, we demonstrate, using a biologically relevant test analyte, the amyloid β-protein (Aβ), a seminal pathologic agent of Alzheimer's disease (AD), that linear relationships exist between (a) peak intensity and concentration at a single plasmonic hot spot smaller than 100 nm, and (b) frequency of hot spots with observable protein signals, i.e. the co-location of an A protein and a hot spot. We demonstrate the detection of Aβ at a concentration as low as 10-18 M after a single 20 �l aliquot of the analyte onto the hybrid platform.

Orientation dependence is also proved by analyzing the standard deviation of spectral feature. The standard deviation in the intensity of individual Raman peaks diminishes for protein size larger than 13 amino acids. Secondary structure of protein (such as protein-protein interaction) remains unchanged regardless of protein orientation. Numerical simulation studies corroborate the experimental observation in that the SERS spectral features of biomedically relevant protein (of larger than 13 amino acids in size, which represent all human protein types) are not affected by the orientation of amino acids randomly dispersed on SERS-active surfaces.

Disease diagnosis has long been a basis of modern medicine, enabling early intervention and effective treatment strategies. Recent advancements in nanotechnology have ushered in a new era of diagnostic techniques, with nanoscale bioparticles emerging as powerful tools in this endeavor. Nanoscale bioparticles, including extracellular vesicles, viruses, and other bioactive entities, have gained prominence due to their unique properties that make them ideal candidates for biomarker detection. These tiny structures, often measuring around 100 nanometers, carry a wealth of molecular information reflective of the physiological and pathological states of the body. Their presence, composition, and abundance in biological fluids such as blood, saliva, and urine hold invaluable clues for diagnosing a wide range of diseases This dissertation presents a cutting-edge approach to disease diagnosis by integrating the analysis of nano bioparticles, Surface-Enhanced Raman Spectroscopy (SERS), and machine learning techniques targeting disease diagnosis. SERS, with its unparalleled sensitivity and specificity, serves as a powerful tool for the characterization of biomolecules. We investigate the feasibility of SERS in capturing the intricate spectral signatures of nano bioparticles, revealing valuable insights into their molecular composition. Moreover, machine learning models are harnessed to decipher this wealth of spectral data, enabling the identification of disease-specific biomarkers with unprecedented accuracy. The article encompasses a detailed exploration of exosome biology, the principles of SERS, the intricacies of machine learning based data analysis methodologies applied to spectral data, preliminary achievements in non-small cell lung cancer diagnostic study, and feasibility of identify SARS-CoV-2 biomarkers for COVID detection. We present a particular subgroup of exosomes derived from human bronchial epithelial cells possessing distinct spectral signatures that can be a potential indicator of non-small cell lung cancer early metastasis, and a rapid and accurate SERS based platform for COVID detection using salivary specimen, superior in some cases to RT-PCR and antigen test. The integration of these multidisciplinary approaches represents a significant step toward revolutionizing disease diagnosis through the convergence of nano bioparticle analysis, spectroscopy, and machine learning, offering a promising avenue for early and accurate disease detection in clinical settings.

Cancer is one of the leading causes of premature death worldwide. Currently, when diagnosed at advanced stages, the five-year survival rates of cancers remain low even with tremendous amounts of medical resources dedicated to the treatments. To enhance the survival rates and reduce the cancer-caused socioeconomic burdens, population-based cancer screening is desired. Such screening requires the detection techniques to be accurate, patient friendly, and easy to operate at low costs. Over the past decade, cell-released small extracellular vesicles (sEVs, sized ⌀ 30−150 nm) have been attracting increasingly amounts of attention as a source of biomarkers for minimally invasive cancer detections. While existing stably in the bodily fluids, sEVs reflect their cells of origins, including cancer cells, through the encapsulated cargos such as DNA/RNA and proteins. Therefore, it is possible to detect cancers by investigating the sEVs captured in the bodily fluids. This thesis aims to explore the potential clinical applicability of analyzing individual sEVs by surface-enhanced Raman spectroscopy (SERS) for minimally invasive cancer detections. To objectively examine the SERS spectral features collected from sEVs, customized machine learning algorithm is utilized. Further, the combination between SERS and machine learning is named as “SERS Identification of Molecules” or “SIM”.The thesis work begins with understanding the heterogeneity of sEVs, namely sEV subpopulations, from the single-vesicle level by SIM. Currently, sEVs, even from the same cell of origin, are recognized to be heterogeneous which can be further sub-fractionated. Size discrepancy is one of the most popularly used criteria for separating the vesicular subpopulations. In Chapter 2, SIM was employed to spectrally detect and analyze individual sEVs from cell lines isolated based on their size discrepancies, forming subpopulations. The results suggested that sEVs in different size groups carried different chemical compositions, which could be reflected by the distinguishable SERS spectral features. Chapter 3 & 4 include the explorations of the clinical applicability of SIM in the sEV-based minimally invasive cancer detections. In the first case, SIM was applied to analyze sEVs derived from the saliva, blood, and tissue samples between gastric cancer (GC) patients and non-GC participants (n = 15 each). The algorithm prediction accuracies were reportedly 90, 85, and 72%. “Leave-a-pair-of-samples out” validation was further performed to test the clinical potential. The area under the curve of each receiver operating characteristic curve was 0.96, 0.91, and 0.65 in tissue, blood, and saliva, respectively. In addition, by comparing the SERS fingerprints of individual vesicles, a possible way of tracing the biogenesis pathways of patient-specific sEVs from tissue to blood to saliva was provided. The second case involved SIM in analyzing sEVs derived from bronchoalveolar fluid (BAL) for non-small cell lung cancer (NSCLC) detection. BAL samples were collected from NSCLC patients and non-cancer participants (n = 10 each). Analyzing the SERS spectra collected from the BAL-derived sEVs revealed the SERS spectral distinguishability between the patient and the control group in general. In addition, the sEV spectra collected from patients with early and late-stage NSCLC were also distinct. Blind test to examine the clinical usage of SIM was perform (n = 6 each for the machine learning model developing and n = 4 for the testing). Under such setup, the model correctly predicted diagnostic results from all the eight individuals in the testing set. Collectively the results obtained from the two cancer case studies indicate the clinical potential of the minimally invasive cancer detections via SIM analyzing sEVs derived from bodily fluids.