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.

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.

Enhancing Efficiency and Stability in Carbon-Based Perovskite Solar Cells by Double Passivation with Ultralow-Cost Coal-Derived Graphene and Its Derivatives.

Coal-derived carbon nanomaterials possess numerous superior features compared to other classic carbon, such as readily accessible surfaces, tunable pore structure, and facile and precise surface functionalization. Therefore, the controllable preparation of coal-derived carbon nanomaterials is anticipated to be of great significance for the performance improvement and commercialization process of carbon-based perovskite solar cells (C-PSCs). In this study, we successfully synthesized highly stable and commercially valuable graphene oxide (GO) and reduced graphene oxide (rGO) utilizing coal. Compared to traditional methods and commercial graphene, the chemical oxidation and pyrolysis process used in this study is mild and simple, offering the advantages of controlled composition and the absence of other impurities. GO or rGO was incorporated into the top of the SnO2 electron transport layer (ETL) of C-PSCs. Under optimized conditions and ultraviolet-ozone (UVO) irradiation, the ultimate power conversion efficiency (PCE) increased from the unmodified 12.4 to 14.04% (based on rGO) and 15.18% (based on GO), representing improvements of 22 and 31%, respectively. The improved photovoltaic performance is mainly owing to enhanced charge transport capabilities, denser interfacial contacts, improved carrier separation properties, increased conductivity, and abundance of hydrophilic functional groups in GO, which can form more stable hydrogen bonds with SnO2. After being stored at room temperature and ambient humidity for 30 days, the modified, unpacked devices retained 87% of the highest power conversion efficiency (PCE). This study introduces a practical and manageable method to enhance the performance of C-PSCs by using functional carbon nanomaterials derived from coal.

The cellulose nanocrystal jammed interfaces induced by CO2-assisted self-assembly for enhancing oil recovery.

Stability of displacement front is of great importance in the immiscible fluid displacement for enhancing oil recovery. Here, a CO2-strenghened assembly approach is demonstrated for the fabrication of highly jammed CNSs (cellulose nanocrystal surfactants) with EPD (N'-ethylpropane-1,3-diamine) and TOCNC (TEMPO oxidized cellulose nanocrystal), which produce a structured film at the oil-water interface to counteract the capillary force, and thus governing the local displacing pattern. In this approach, EPD molecules can be deeply protonated in the presence of CO2, favoring their binding forces with TOCNC at the interface to produce more CNSs. Meanwhile, the strong intermolecular attractions among CO2-bearing CNSs promote to form a striped interfacial film with both the close-packed rod-like arrays in horizontal and the multi-layer in lateral. Further, the CNSs-based film confers with a high strength and elasticity can reduce the capillary force by 87 % in micro-channels, yielding a smooth water-to-oil displacement front, which markedly enhances the oil recovery by 20.6 % compared to the surfactant-only flooding. This self-assembly strategy has a great implication in eco-friendly and cost-effective applications, such as enhanced oil recovery, CO2 geo-sequestration, and water infiltration.

Constructing a novel type-â ¡ ZnO/BiOCOOH heterojunction microspheres for the degradation of tetracycline and bacterial inactivation.

A novel ZnO/BiOCOOH microsphere photocatalyst with a type-Ⅱ mechanism was developed for the first time. This strategy was accomplished by immobilizing ZnO onto 3D BiOCOOH microspheres via a single-step hydrothermal synthesis method. The ability to degrade tetracycline (TC) in water under visible light and inactivate bacteria of as-catalyst were analyzed. Among the prepared samples, the ZnO/BiOCOOH composite, with a mass ratio of 40%(Zn/Bi), exhibited the highest photocatalytic activity, which was able to degrade 98.22% of TC in just 90 min and completely eradicated Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) in 48 h, and had potential application in solving water resource environmental pollution. The photoelectric characteristics of the photocatalysts were examined by means of electrochemical impedance spectroscopy (EIS) and photoluminescence (PL) spectroscopy. The findings indicated that the superior photocatalytic performance could be credited to the dissociation of electrons (e-) and holes (h+) in heterojunction composites. Finally, electron paramagnetic resonance (EPR) and capture experiments were conducted to confirm the photocatalytic mechanism of the type-Ⅱ heterojunction. This work provides a new Bi-base photocatalyst for aqueous environmental control.

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.

Water-Induced Cellulose Nanofibers/Poly(vinyl alcohol) Hydrogels Regulated by Hydrogen Bonding for In Situ Water Shutoff.

Hydrogels have been widely applied to the water shutoff in oilfields due to their excellent three-dimensional network and thermal and physicochemical stability, and it is still a huge challenge to develop new hydrogels with simple preparation, low cost, and high mechanical performance that can meet the requirements of practical applications. Herein, we devised a simple and universal manufacturing method for regulating the hydrogen bonds between poly(vinyl alcohol) (PVA) and cellulose nanofibers (CNF) via the water-diffusion action, thus fabricating a physically tough PVA-CNF hydrogel for the in situ water shutoff. This method allowed the polymer chains to strengthen the molecular interactions between polymers upon replacing with water (a poor solvent) to regulate the cross-linking structure, characterizing by the nano-crystallinity domains and fibrillar segments, which also accounted for the thermal stability, extraordinary elasticity, high stretchability, and toughness of PVA-CNF hydrogel. Further, the obtained PVA-CNF hydrogel exhibited superb plugging performance, that is, the breakthrough pressure gradient could reach 71.56 MPa·m-1, surpassing all currently reported gelling water shutoff agents. This water-induced in situ hydrogelation made it well suited as a water shutoff agent in oilfields and may provide a promising strategy to fabricate mechanically robust smart materials for the water shutoff projects with low cost, simple processing, and high efficiency.

High stretchable and self-healing nanocellulose-poly(acrylic acid) composite hydrogels for sustainable CO2 shutoff.

The injection of CO2 into oil reservoirs to enhance oil recovery (EOR) has become a widely accepted and effective technical method, which, however, remains subject to the gas channeling caused by the reservoir fractures. Herein, this work developed a novel plugging gel combining excellent mechanical properties, fatigue resistance, elastic and self-healing properties for the CO2 shutoff purpose. This gel consisting of grafted nanocellulose and polymer network was synthesized via a free-radical polymerization, and reinforced by using Fe3+ to cross-link the two networks. The as-prepared PAA-TOCNF-Fe3+ gel has a stress of 1.03 MPa and a high strain of 1491 %, and self-heals to its original 98 % in stress and 96 % in strain after rupture, respectively. The introduce of TOCNF/Fe3+ improves the excellent energy dissipation and self-healing via the synergy effect of dynamical coordination bonds and hydrogen bonds. Further, the PAA-TOCNF-Fe3+ gel is both flexible and high-strength in plugging the multi-round CO2 injection, during which the CO2 breakthrough pressure is above 9.9 MPa/m, the plugging efficiency exceeds 96 %, and the self-healing rate is larger than 90 %. Given that above, this gel shows a great potential to plug the high-pressure CO2 flow, which could offer a new method for CO2-EOR and carbon storage.

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).

Colorimetric and visual determination of iodide ions via morphology transition of gold nanobipyramids.

The gold nanobipyramids (Au NBPs) are widely used in the analytical detection of biochemistry due to their unique localized surface plasmon resonance (LSPR) properties. In our developed approach, I- in kelp was detected by etching Au NBPs in the presence of IO3-. Under acidic conditions, IO3- reacted rapidly with I- to form I2, subsequently I2 reacted with I- to form the intermediate I3-. In the presence of CTAB, Au NBPs were etched by I2 derived from I3-, resulting in a decrease in the aspect ratio of Au NBPs, to form a significant blue shift of LSPR longitudinal peak and color variation of colloid which changed from blue-green to magenta and could be employed to quantitatively detect the concentration of I- with the naked eye. A linear relationship can be found between the LSPR peak changes with the I- concentration in a wide range from 4.0 µM to 15.0 µM, and the sensitive limit of detection (LOD) was 0.2 µM for UV-vis spectroscopy and the obvious color changes with a visual LOD was 4.0 µM for the naked eye. Benefiting from the high specificity, the proposed colorimetric detection of I- in kelp samples was achieved, indicating the available potential of the colorimetric detection for the determination of I- in real samples. What's more, this detection procedure was time-saving and could avoid tedious procedures.

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.

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.

Liquid Foam Stabilized by a CO2-Responsive Surfactant and Similarly Charged Cellulose Nanofibers for Reversibly Plugging in Porous Media.

CO2 foams are of great importance in oil recovery but challenging in some aspects like long-term stabilization and time-separated conflict. In this work, a stability-enhanced switchable foam was fabricated using bis-(2-hydroxyethoxy) olefine amine (BOA) and trace amounts (0.05 wt %) of cationic-modified cellulose nanofibers (CCNFs). The CCNF was developed using sequentially functionalized CNF with diamine groups, which were essential to promote the aqueous dispersibility and a key for strengthening the stabilization of foam. The combination of similarly charged CCNFs and BOA in the presence of CO2 contributed to both surface activity and viscoelasticity. It was demonstrated that CCNFs were entangled and stacked to form the compact films and possessed the ability to costabilize the lamellae, as observed by microscopic studies. In addition, the intermolecular H-bonds were promoted in the binary system after being protonated by CO2 and thus balancing the electrostatic forces, as explored by spectroscopy characterizations. The soft fibrous structure of the CCNF was also capable of wrapping gas bubbles in the form of a functional membrane with both low gas permeability and high surface potential, which slowed down the coarsening and coalescence. Of particular interest is that the reversible protonation state of CCNF-BOA complexes upon the alternate treatment with CO2/N2 led to reversible fast foaming/defoaming, which would be beneficial to construct the steerable plugging in the sand pack. This work is expected to provide a new direction and application of the CO2 responsive foam stabilized by similarly charged nanocellulose fibers in oilfield development.

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.

Smart ultra-stable foams stabilized using cellulose nanocrystal (CNC) gels via noncovalent bonding.

Smart ultra-stable foams stabilized by cellulose nanocrystal (CNC)-based gels were fabricated. The stabilization is ascribed to the dense films and three-dimensional networks at the interface and in the bulk induced by the charge shielding effect and electrostatic attraction between protonated bis(2-hydroxyethyl)oleylamine (BOA-H+) micelles and negatively charged CNC colloids. The as-prepared foam could maintain its morphology without breaking or drainage for two months, showing high stability. Outstanding CO2/N2 reversibility endows the system with on-demand control of foaming/defoaming, which is necessary in many aspects. The functionalized foam is expected to open up an opportunity for the design of intelligent oilfield chemicals and extinguishant systems.

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.

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.

Design of infrared radiation diagnostic calorimeter for the prototype radio frequency driven negative ion source for neutral beam injection.

In order to study the generation and extraction of negative ions for neutral beam injection application, a prototype radio frequency driven negative ion source and the corresponding test bench are under construction at the Institute of Plasma Physics, Chinese Academy of Sciences. A new design of infrared radiation diagnostic calorimeter for testing beam characteristics is put forward. Compared with the conventional calorimeter, the calorimeter adopts the block structure (8 × 28 tungsten hexahedron blocks) and modularization design (4 modules), so it has higher precision and good scalability. The thermal performance of the calorimeter is assessed using a finite element method. Simulation results show that the design can be achieved to operate in the stable-state mode at the maximum thermal flux 6.45 MW/m2 and meet the full requirement of beam diagnosis.

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.