Molecular orientation and specificity in the identification of biomolecules via surface enhanced Raman spectroscopy.

The orientation dependence of the Raman spectral features of individual protein/biomolecules is studied using surface-enhanced Raman spectroscopy (SERS). Large variation in spectral features mainly in term of peak intensity is observed from small proteins/peptides. We aim to address the question of whether the spectral features of SERS are uniquely determined by the type of protein/molecules or are influenced prominently by factors more than the identity of the molecules such as orientation of molecules relative to the substrate surface. 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. These findings are instrumental to understanding the exceedingly high (label-free) specificity when SERS is used in identifying proteins/peptides as can be found in numerous publications from different research groups in both in vivo and in vitro analyses. It was noted that the spectral position of all Raman peaks assignable to the various amino acids are independent of molecule orientation even though their intensities do vary.

Crucial Impact of Hydrophilicity on the Self-Assembled 2D Colloidal Crystals Using Langmuir-Blodgett Method.

Large-scale close-packed two-dimensional (2D) colloidal crystal with high coverage is indispensable for various promising applications. The Langmuir-Blodgett (LB) method is a powerful technique to prepare 2D colloidal crystals. However, the self-assembly and movement of microspheres during the whole LB process are less analyzed. In this study, we clarify the crucial impact of hydrophilicity of the microspheres on their self-assembly in the LB process and on the properties of the prepared 2D colloidal crystals. The characteristic surface pressure-area isotherms of the microspheres have been analyzed and adjusted by only counting the quantity of the microspheres on the water surface, which leads to more accurate results. The critical surface pressures for hydrophilic and hydrophobic microspheres are about 61 and 46 mN/m, respectively. The decrease of the surface hydrophilicity of microspheres facilitates their self-assembly on the water surface, which further leads to higher coverage and less defects of the 2D colloidal crystals. A coverage of as high as 97% was obtained using hydrophobic microspheres. Entropy and intersphere capillary forces drive the self-assembly and transportation of the microspheres, respectively. Caused by the diffraction of visible light, opposite contrasts at local adjacent regions on the surface of the 2D colloidal crystals have been observed. The understanding of self-assembly of the microspheres during the LB process paves the way to fabricate the high-quality 2D colloidal crystals for various applications such as photonic papers and inks, stealth materials, biomimetic coatings, and related nanostructures.

Reliable resistive switching of epitaxial single crystalline cubic Y-HfO2 RRAMs with Si as bottom electrodes.

Previous studies have mainly focused on the resistive switching (RS) of amorphous or polycrystalline HfO2-RRAM. The RS of single crystalline HfO2 films has been rarely reported. Yttrium doped HfO2 (YDH) thin films were fabricated and successful Y incorporation into HfO2 was confirmed by x-ray photoemission spectroscopy. A pure cubic phase of YDH and an abrupt YDH/Si interface were obtained and verified by x-ray diffraction, Raman spectroscopy and transmission electron microscopy. A Pt/YDH/n++-Si heterostructure using Si as the bottom electrode was fabricated, which shows stable RS with an ON/OFF ratio of 100 and a reliable data retention (104 s). The electron transport mechanism was investigated in detail. It indicates that hopping conduction is dominating when the device is at a high resistance state, while space charge limited conduction acts as the dominant factor at a low resistance state. Such behavior, which is different from devices using TiN or Ti as electrodes, was attributed to the Y doping and specific YDH/Si interface. Our results demonstrate a proof of concept study to use highly doped Si as bottom electrodes along with single crystalline YDH as insulator layer for such RRAM applications as wireless sensors and synaptic simulation.

Interfacial States and Fano-Feshbach Resonance in Graphene-Silicon Vertical Junction.

Interfacial quantum states are drawing tremendous attention recently because of their importance in design of low-dimensional quantum heterostructures with desired charge, spin, or topological properties. Although most studies of the interfacial exchange interactions were mainly performed across the interface vertically, the lateral transport nowadays is still a major experimental method to probe these interactions indirectly. In this Letter, we fabricated a graphene and hydrogen passivated silicon interface to study the interfacial exchange processes. For the first time we found and confirmed a novel interfacial quantum state, which is specific to the 2D-3D interface. The vertically propagating electrons from silicon to graphene result in electron oscillation states at the 2D-3D interface. A harmonic oscillator model is used to explain this interfacial state. In addition, the interaction between this interfacial state (discrete energy spectrum) and the lateral band structure of graphene (continuous energy spectrum) results in Fano-Feshbach resonance. Our results show that the conventional description of the interfacial interaction in low-dimensional systems is valid only in considering the lateral band structure and its density-of-states and is incomplete for the ease of vertical transport. Our experimental observation and theoretical explanation provide more insightful understanding of various interfacial effects in low-dimensional materials, such as proximity effect, quantum tunneling, etc. More important, the Fano-Feshbach resonance may be used to realize all solid-state and scalable quantum interferometers.

Copper Ion Binding Site in ß-Amyloid Peptide.

ß-Amyloid aggregates in the brain play critical roles in Alzheimer's disease, a chronic neurodegenerative condition. Amyloid-associated metal ions, particularly zinc and copper ions, have been implicated in disease pathogenesis. Despite the importance of such ions, the binding sites on the ß-amyloid peptide remain poorly understood. In this study, we use scanning tunneling microscopy, circular dichroism, and surface-enhanced Raman spectroscopy to probe the interactions between Cu2+ ions and a key ß-amyloid peptide fragment, consisting of the first 16 amino acids, and define the copper-peptide binding site. We observe that in the presence of Cu2+, this peptide fragment forms ß-sheets, not seen without the metal ion. By imaging with scanning tunneling microscopy, we are able to identify the binding site, which involves two histidine residues, His13 and His14. We conclude that the binding of copper to these residues creates an interstrand histidine brace, which enables the formation of ß-sheets.

Label-Free SERS Selective Detection of Dopamine and Serotonin Using Graphene-Au Nanopyramid Heterostructure.

Ultrasensitive detection and spatially resolved mapping of neurotransmitters, dopamine and serotonin, are critical to facilitate understanding brain functions and investigate the information processing in neural networks. In this work, we demonstrated single molecule detection of dopamine and serotonin using a graphene-Au nanopyramid heterostructure platform. The quasi-periodic Au structure boosts high-density and high-homogeneity hotspots resulting in ultrahigh sensitivity with a surface enhanced Raman spectroscopic (SERS) enhancement factor ∼10(10). A single layer graphene superimposed on a Au structure not only can locate SERS hot spots but also modify the surface chemistry to realize selective enhancement Raman yield. Dopamine and serotonin could be detected and distinguished from each other at 10(-10) M level in 1 s data acquisition time without any pretreatment and labeling process. Moreover, the heterostructure realized nanomolar detection of neurotransmitters in the presence of simulated body fluids. These findings represent a step forward in enabling in-depth studies of neurological processes including those closely related to brain activity mapping (BAM).

Stimulated emission in GaN-based laser diodes far below the threshold region.

We identify that the stimulated emission of GaN laser diodes (LDs) emerges far below the traditionally recognized threshold from both optical and electrical experiments. Below the threshold, the linear-polarized stimulated emission has been the dominating part of overall emission and closely related to resonant cavity. Its intensity increases super linearly with current while that of spontaneous emission increases almost linearly. Moreover, the separation of quasi-Fermi levels of electrons and holes across the active region has already exceeded the photon emission energy, namely, realized the population-inversion.

AI-Driven Spectral Decomposition: Predicting the Most Probable Protein Compositions from Surface Enhanced Raman Spectroscopy Spectra of Amino Acids.

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for elucidating the molecular makeup of materials. It possesses the unique characteristics of single-molecule sensitivity and extremely high specificity. However, the true potential of SERS, particularly in capturing the biochemical content of particles, remains underexplored. In this study, we harnessed transformer neural networks to interpret SERS spectra, aiming to discern the amino acid profiles within proteins. By training the network on the SERS profiles of 20 amino acids of human proteins, we explore the feasibility of predicting the predominant proteins within the µL-scale detection volume of SERS. Our results highlight a consistent alignment between the model's predictions and the protein's known amino acid compositions, deepening our understanding of the inherent information contained within SERS spectra. For instance, the model achieved low root mean square error (RMSE) scores and minimal deviation in the prediction of amino acid compositions for proteins such as Bovine Serum Albumin (BSA), ACE2 protein, and CD63 antigen. This novel methodology offers a robust avenue not only for protein analytics but also sets a precedent for the broader realm of spectral analyses across diverse material categories. It represents a solid step forward to establishing SERS-based proteomics.

Label-free single-vesicle based surface enhanced Raman spectroscopy: A robust approach for investigating the biomolecular composition of small extracellular vesicles.

Small extracellular vesicles (sEVs) are cell-released vesicles ranging from 30-150nm in size. They have garnered increasing attention because of their potential for both the diagnosis and treatment of disease. The diversity of sEVs derives from their biological composition and cargo content. Currently, the isolation of sEV subpopulations is primarily based on bio-physical and affinity-based approaches. Since a standardized definition for sEV subpopulations is yet to be fully established, it is important to further investigate the correlation between the biomolecular composition of sEVs and their physical properties. In this study, we employed a platform combining single-vesicle surface-enhanced Raman spectroscopy (SERS) and machine learning to examine individual sEVs isolated by size-exclusion chromatography (SEC). The biomolecular composition of each vesicle examined was reflected by its corresponding SERS spectral features (biomolecular "fingerprints"), with their roots in the composition of their collective Raman-active bonds. Origins of the SERS spectral features were validated through a comparative analysis between SERS and mass spectrometry (MS). SERS fingerprinting of individual vesicles was effective in overcoming the challenges posed by EV population averaging, allowing for the possibility of analyzing the variations in biomolecular composition between the vesicles of similar and/or different sizes. Using this approach, we uncovered that each of the size-based fractions of sEVs contained particles with predominantly similar SERS spectral features. Indeed, more than 84% of the vesicles residing within a particular group were clearly distinguishable from that of the other EV sub-populations, despite some spectral variations within each sub-population. Our results suggest the possibility that size-based EV fractionation methods produce samples where similarly eluted sEVs are correlated with their respective biochemical contents, as reflected by their SERS spectra. Our findings therefore highlight the possibility that the biogenesis and respective biological functionalities of the various sEV fractions may be inherently different.

Controlling the Nucleation and Growth of Salt from Bodily Fluid for Enhanced Biosensing Applications.

Surface-enhanced Raman spectroscopy (SERS) represents a transformative tool in medical diagnostics, particularly for the early detection of key biomarkers such as small extracellular vesicles (sEVs). Its unparalleled sensitivity and compatibility with intricate biological samples make it an ideal candidate for revolutionizing noninvasive diagnostic methods. However, a significant challenge that mars its efficacy is the throughput limitation, primarily anchored in the prerequisite of hotspot and sEV colocalization within a minuscule range. This paper delves deep into this issue, introducing a never-attempted-before approach which harnesses the principles of crystallization-nucleation and growth. By synergistically coupling lasers with plasmonic resonances, we navigate the challenges associated with the analyte droplet drying method and the notorious coffee ring effect. Our method, rooted in a profound understanding of crystallization's materials science, exhibits the potential to significantly increase the areal density of accessible plasmonic hotspots and efficiently guide exosomes to defined regions. In doing so, we not only overcome the throughput challenge but also promise a paradigm shift in the arena of minimally invasive biosensing, ushering in advanced diagnostic capabilities for life-threatening diseases.

Analyzing bronchoalveolar fluid derived small extracellular vesicles using single-vesicle SERS for non-small cell lung cancer detection.

An emerging body of research by biologists and clinicians has demonstrated the clinical application of small extracellular vesicles (sEVs, also commonly referred to as exosomes) as biomarkers for cancer detections. sEVs isolated from various body fluids such as blood, saliva, urine, and cerebrospinal fluid have been used for biomarker discoveries with highly encouraging outcomes. Among the biomarkers discovered are those responsible for multiple cancer types and immune responses. These biomarkers are recapitulated from the tumor microenvironments. Yet, despite numerous discussions of sEVs in scientific literature, sEV-based biomarkers have so far played only a minor role for cancer diagnostics in the clinical setting, notably less so than other techniques such as imaging and biopsy. In this paper, we report the results of a pilot study (n = 10 from each of the patient and the control group) using bronchoalveolar lavage fluid to determine the presence of sEVs related to non-small cell lung cancer in twenty clinical samples examined using surface enhanced Raman spectroscopy (SERS).

The Feasibility of Early Alzheimer's Disease Diagnosis Using a Neural Network Hybrid Platform.

Early diagnosis of Alzheimer's Disease (AD) is critical for disease prevention and cure. However, currently, techniques with the required high sensitivity and specificity are lacking. Recently, with the advances and increased accessibility of data analysis tools, such as machine learning, research efforts have increasingly focused on using these computational methods to solve this challenge. Here, we demonstrate a convolutional neural network (CNN)-based AD diagnosis approach using the surface-enhanced Raman spectroscopy (SERS) fingerprints of human cerebrospinal fluid (CSF). SERS and CNN were combined for biomarker detection to analyze disease-associated biochemical changes in the CSF. We achieved very high reproducibility in double-blind experiments for testing the feasibility of our system on human samples. We achieved an overall accuracy of 92% (100% for normal individuals and 88.9% for AD individuals) based on the clinical diagnosis. Further, we observed an excellent correlation coefficient between our test score and the Clinical Dementia Rating (CDR) score. Our findings offer a substantial indication of the feasibility of detecting AD biomarkers using the innovative combination of SERS and machine learning. We are hoping that this will serve as an incentive for future research in the field.

Multiwavelength High-Detectivity MoS2 Photodetectors with Schottky Contacts.

Photodetection is one of the vital functions for the multifunctional "More than Moore" (MtM) microchips urgently required by Internet of Things (IoT) and artificial intelligence (AI) applications. The further improvement of the performance of photodetectors faces various challenges, including materials, fabrication processes, and device structures. We demonstrate in this work MoS2 photodetectors with a nanoscale channel length and a back-gate device structure. With the mechanically exfoliated six-monolayer-thick MoS2, a Schottky contact between source/drain electrodes and MoS2, a high responsivity of 4.1 × 103 A W-1, and a detectivity of 1.34 × 1013 cm Hz1/2 W-1 at 650 nm were achieved. The devices are also sensitive to multiwavelength lights, including 520 and 405 nm. The electrical and optoelectronic properties of the MoS2 photodetectors were studied in depth, and the working mechanism of the devices was analyzed. The photoinduced Schottky barrier lowering (PIBL) was found to be important for the high performance of the phototransistor.

Gold Nanopyramid Arrays for Non-Invasive Surface-Enhanced Raman Spectroscopy-Based Gastric Cancer Detection via sEVs.

Gastric cancer (GC) is one of the most common and lethal types of cancer affecting over one million people, leading to 768,793 deaths globally in 2020 alone. The key for improving the survival rate lies in reliable screening and early diagnosis. Existing techniques including barium-meal gastric photofluorography and upper endoscopy can be costly and time-consuming and are thus impractical for population screening. We look instead for small extracellular vesicles (sEVs, currently also referred as exosomes) sized ⌀ 30-150 nm as a candidate. sEVs have attracted a significantly higher level of attention during the past decade or two because of their potentials in disease diagnoses and therapeutics. Here, we report that the composition information of the collective Raman-active bonds inside sEVs of human donors obtained by surface-enhanced Raman spectroscopy (SERS) holds the potential for non-invasive GC detection. SERS was triggered by the substrate of gold nanopyramid arrays we developed previously. A machine learning-based spectral feature analysis algorithm was developed for objectively distinguishing the cancer-derived sEVs from those of the non-cancer sub-population. sEVs from the tissue, blood, and saliva of GC patients and non-GC participants were collected (n = 15 each) and analyzed. 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, we provided a possible way of tracing the biogenesis pathways of patient-specific sEVs from tissue to blood to saliva. The methodology involved in this study is expected to be amenable for non-invasive detection of diseases other than GC.

Highly heterogeneous epitaxy of flexoelectric BaTiO3-δ membrane on Ge.

The integration of complex oxides with a wide spectrum of functionalities on Si, Ge and flexible substrates is highly demanded for functional devices in information technology. We demonstrate the remote epitaxy of BaTiO3 (BTO) on Ge using a graphene intermediate layer, which forms a prototype of highly heterogeneous epitaxial systems. The Ge surface orientation dictates the outcome of remote epitaxy. Single crystalline epitaxial BTO3-δ films were grown on graphene/Ge (011), whereas graphene/Ge (001) led to textured films. The graphene plays an important role in surface passivation. The remote epitaxial deposition of BTO3-δ follows the Volmer-Weber growth mode, with the strain being partially relaxed at the very beginning of the growth. Such BTO3-δ films can be easily exfoliated and transferred to arbitrary substrates like Si and flexible polyimide. The transferred BTO3-δ films possess enhanced flexoelectric properties with a gauge factor of as high as 1127. These results not only expand the understanding of heteroepitaxy, but also open a pathway for the applications of devices based on complex oxides.

Hybrid System Combining Two-Dimensional Materials and Ferroelectrics and Its Application in Photodetection.

Photodetectors are one of the most important components for a future "Internet-of-Things" information society. Compared to the mainstream semiconductor-based photodetectors, emerging devices based on two-dimensional (2D) materials and ferroelectrics as well as their hybrid systems have been extensively studied in recent decades due to their outstanding performances and related interesting physical, electrical, and optoelectronic phenomena. In this paper, we review the photodetection based on 2D materials and ferroelectric hybrid systems. The fundamentals of 2D and ferroelectric materials as well as the interaction in the hybrid system will be introduced. Ferroelectricity modulated optoelectronic properties in the hybrid system will be discussed in detail. After the basics and figures of merit of photodetectors are summarized, the 2D-ferroelectrics devices with different structures including p-n diodes, Schottky diodes, and field-effect transistors will be reviewed and compared. The polarization of ferroelectrics offers the possibility of the modulation and enhancement of the photodetection in the hybrid detectors, which will be discussed in depth. Finally, the challenges and perspectives of the photodetectors based on 2D ferroelectrics will be proposed. This Review outlines the important aspects of the recent development of the hybrid system of 2D and ferroelectric materials, which could interact with each other and thus lead to photodetectors with higher performances. Such a Review will be helpful for the research of emerging physical phenomena and for the design of multifunctional nanoscale electronic and optoelectronic devices.

Na-doped p-type ZnO microwires.

p-Type ZnO microwires were first synthesized by a simple chemical vapor deposition method using Na as the dopant source. p-Type doping was confirmed by the electrical transport in single-wire field-effect transistors and low-temperature photoluminescence. The carrier mobility of the microwires was estimated to be approximately 2.1 cm(2) V(-1) S(-1).

Ultrasensitive amyloid ß-protein quantification with high dynamic range using a hybrid graphene-gold surface-enhanced Raman spectroscopy platform.

Surface enhanced Raman spectroscopy (SERS) holds great promise in biosensing because of its single-molecule, label-free sensitivity. We describe here the use of a graphene-gold hybrid plasmonic platform that enables quantitative SERS measurement. Quantification is enabled by normalizing analyte peak intensities to that of the graphene G peak. We show that two complementary quantification modes are intrinsic features of the platform, and that through their combined use, the platform enables accurate determination of analyte concentration over a concentration range spanning seven orders of magnitude. We demonstrate, using a biologically relevant test analyte, the amyloid ß-protein (Aß), 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 Aß protein and a hot spot. We demonstrate the detection of Aß at a concentration as low as 10-18 M after a single 20 µl aliquot of the analyte onto the hybrid platform. This detection sensitivity can be improved further through multiple applications of analyte to the platform and by rastering the laser beam with smaller step sizes.

One-Pot Self-Templated Growth of Gold Nanoframes for Enhanced Surface-Enhanced Raman Scattering Performance.

As one of the representative metallic hollow nanostructures, Au nanoframes have shown fascinating properties such as strong localized surface plasmon resonance associated with emerging applications such as surface-enhanced Raman scattering (SERS) sensors. In this study, for the first time, a facile one-pot synthetic approach for hollow Au nanoframes is demonstrated by directly etching Au nanoplates, that is, the so-called self-templates. A novel growth mechanism has been revealed that involves a synergistic function of Ag and Br ions. The presence of Ag+ leads to the observation of self-limiting Au film thickness, whereas Au{111} facets are preferentially attacked by the presence of Br- in the reaction ambient. More importantly, graphene is introduced to prevent/minimize aggregation during the formation of Au nanoframes. The combined simulation and experimental studies show 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 SERS technique.

Toward a Reliable Synaptic Simulation Using Al-Doped HfO2 RRAM.

The potential in a synaptic simulation for neuromorphic computation has revived the research interest of resistive random access memory (RRAM). However, novel applications require reliable multilevel resistive switching (RS), which still represents a challenge. We demonstrate in this work the achievement of reliable HfO2-based RRAM devices for synaptic simulation by performing the Al doping and the postdeposition annealing (PDA). Transmission electron microscopy and operando hard X-ray photoelectron spectroscopy results reveal the positive impact of Al doping on the formation of oxygen vacancies. Detailed I-V characterizations demonstrate that the 16.5% Al doping concentration leads to better RS properties of the device. In comparison with the other reported results based on HfO2 RRAM, our devices with 16.5% Al-doping and PDA at 450 °C show better reliable multilevel RS (∼20 levels) performance and an increased on/off ratio. The 16.5% Al:HfO2 sample with PDA at 450 °C shows good potentiation/depression characteristics with low pulse width (10 µs) along with a good On/Off ratio (>1000), good data retention at room temperature, and high temperature and good program/erase endurance characteristics with a pulse width of 50 ns. The synapse features including potentiation, depression, and spike time-dependent plasticity were successfully achieved using optimized Al-HfO2 RRAM devices. Our results demonstrate the beneficial effects of Al doping and PDA on the enhancement of the performances of RRAM devices for the synaptic simulation in neuromorphic computing applications.