Glaucoma is a leading cause of irreversible blindness worldwide, with elevated intraocular pressure (IOP) serving as the major risk factor for disease onset and progression. IOP is largely determined by aqueous humor outflow resistance in the conventional pathway, where interactions between trabecular meshwork (TM) cells and the surrounding extracellular matrix (ECM) are pivotal. In this study, we examined the segmental mechanobiology of normal and glaucomatous human TM cells isolated from high-flow (HF) and low-flow (LF) regions of the outflow pathway of normal and primary open-angle glaucoma (both male and female). Cells were seeded on type I collagen gels with fibrillar elastic moduli of 4.7 kPa and 27.7 kPa to approximate the ECM conditions of normal and glaucomatous eyes, respectively. We employed 3D traction force microscopy to quantify time-dependent contractile forces in the cells as well as curl, divergence, orientation, and tensile strain in the collagen fibers within 12 h postseeding. We also analyzed the organization of actin, microtubule, and intermediate filaments in normal and glaucomatous TM cells from both HF and LF regions. Multivariable mixed-effects models showed that, after accounting for fibrillar stiffness, time, normal/glaucoma status, and sex, HF cells generated ∼1.6-fold higher mean traction (window averaged, ∼5-6 cells) and ∼1.4-fold higher tensile strain than LF cells and induced ∼1.5-fold greater collagen curl (p < 0.001 for all), whereas divergence and glaucoma versus normal difference were not significant in this data set. Stiffer gels increased the collagen fibril preferred orientation angle (HF > LF), and glaucomatous LF cells showed persistently disorganized actin and microtubules with little change in intermediate filaments. These findings emphasize the importance of segmental cell-ECM interactions in modulating cell-ECM interaction.
Mediated electron transfer (MET) plays a crucial role in energy storage and conversion technologies such as redox targeting flow batteries (RTFBs), yet its experimental investigation often requires labor-intensive and low-throughput setups. To address this, we developed a microfabricated interdigitated electrode array (IDA) platform that enables automated, high-throughput electrochemical redox titration measurement to be performed to study the MET process. Our redox titration method enables simultaneous measurement of the charge capacity and rate of MET processes on a material or surface. Automated redox titration (ART) facilitates systematic investigation of the MET process across a broad parameter space, exemplified through the study of polypyrrole (PPy) and a pyrene-4,5,9,10-tetrone azo group-based polymer (PTAP), both redox-active polymers relevant to various energy storage applications. Using PPy as a model material, 500 redox titration measurements were conducted within 50 h, varying the electrode gap widths, polymer charging potentials, voltammetric scan rates, and electrolyte concentrations. Finite-element simulations confirmed the electrochemical responses and elucidated the kinetics of the MET reactions. Our automated methodology was further tested with PTAP, revealing a surprising charging potential dependence on the rate of MET. The automation, flexibility, and scalability of our redox titration platform pave the way not only for advanced studies of MET processes relevant to RTFBs, but also with implications in the understanding of next-generation energy storage materials, molecular electrocatalysis, and biosensing.
Liquid-phase transmission electron microscopy (LPTEM) offers critical insights into the dynamic behaviors of materials, but automated analysis is hindered by the scarcity of annotated data and the high cost of image acquisition. To address these challenges, we propose an LPTEM deep learning framework that (i) closes the annotation gap, (ii) localizes nanoparticles through real-time detection, and (iii) preserves per-pixel detail. Through CycleGAN, we are able to generate a large number of images that are style-consistent with real LPTEM frames, effectively expanding the data set without manual labeling. After augmenting the data set, our fine-tuned YOLOv11n model achieves 97.66% precision and 99.05% mAP50 for detecting nanoparticles, closely approaching the performance of models trained on real data. Three Mobile-UNet variants, optimized for different computational efficiency and accuracy trade-offs, demonstrate instance segmentation for in situ TEM with intersection over union (IoU) scores ranging from 0.8685 to 0.9207 (on a scale where 1.0 indicates perfect overlap with the ground truth), indicating highly accurate particle shapes. Our integrated framework significantly enhances real-time detection and segmentation performance of nanoparticles in LPTEM, with YOLO + Mobile-UNet Slim achieving a real-time processing speed of 102.34 FPS. This framework establishes a scalable, annotation-efficient route to high-precision, real-time LPTEM analysis, opening the door to autonomous in situ experiments and accelerated materials discovery.
Advances in high-throughput mass spectrometry have shifted the bottleneck in plasma proteomics from data acquisition to sample preparation. While enrichment and depletion strategies enable detection of low-abundance proteins, their complexity and cost limit scalability and clinical translation. Targeting midto-high abundance proteins from neat plasma offers a practical, reproducible alternative aligned with clinical workflows. Here, we combine fully automated sample preparation and Evotip loading on the Bravo AssayMAP system with extensive method optimization on the timsTOF HT and gas-phase fractionation deep spectral libraries to advance neat plasma proteomics. Automation reduced hands-on time by 88% and significantly improved robustness. Mixed-mode searching with a 1788-protein library increased identifications by up to 31% at a throughput of 100 samples per day, with less than 15% variation across plates. In a coronary artery disease cohort, we quantified 936 biologically relevant proteins and found 42 dysregulated compared to healthy controls. This streamlined, high-throughput workflow enables deep, reproducible analysis of neat plasma at scale, paving the way for population-level biomarker discovery and clinical implementation.
Tumor spheroid growth assays are used to evaluate the potential of cancer therapies in vitro. During such experiments, extensive microscopic image series are generated, which are commonly analyzed using threshold-based delineations. However, due to treatment-induced morphological changes of the spheroids, very time-consuming manual corrections are often required. The goal of our work was the development of an AI-based method for accurate and automated delineation of spheroid growth assays, ultimately reducing the reliance on manual delineation and corrections. Spheroids were grown from mouse pheochromocytoma (MPC) cells and subjected to irradiation with particle-emitting radioligands. Spheroid growth was monitored over 35 days. N = 38090 images, acquired within seven experiments and two studies, were included. Spheroids were delineated with a threshold-based method followed by manual corrections and the resulting delineations served as ground truth for network training and testing. The data were divided into two independent data sets: one for training and internal validation using a 5-fold cross-validation (N = 21567; main data set) and another for final independent testing (N = 16523). The network was developed using the nnU-Net v2 deep-learning (DL) framework. DL-based and manual delineations were compared using the Dice similarity coefficient (DSC). Additionally, treatment effects in a spheroid experiment were compared by quantifying half-maximum spheroid control doses (SCD50). The median DSC values in the main and test data sets were 0.979 and 0.974, respectively. In the main data set, only 7% (N = 1571) of the DL-generated delineations and 8% (N = 1304) in the test data set showed DSC < 0.9, indicating high performance. The SCD50 values were comparable between manual (day 13: 0.086 ± 0.001, day 35: 0.150 ± 0.001) and DL-based delineations (day 13: 0.083 ± 0.002, day 35: 0.149 ± 0.007). The network enables fast and accurate delineation of tumor spheroids in treatment response assays, reducing the time needed to delineate all spheroid images of a single experiment from several days with the previously applied method to a few hours only.
Seawater pH is a critical parameter influencing many processes in the ocean. Today it is mainly measured by indicator-based spectrophotometry to allow for high precision. This, however, is at the expense of traceability and systematic errors originating from changes in temperature, salinity, and other matrix effects. Moreover, in routine practice, this approach is not performed in situ and requires sampling and manual manipulations, which are prone to introduce additional errors, including gas exchange with the atmosphere. Unfortunately, in the last few decades, the electrochemical sensing community has failed to make efforts to improve the performance of the main method, which is potentiometric detection with pH glass electrodes. To address this, we aim here to improve the sensitivity and precision of submersible pH probes on the basis of pH glass electrodes by minimizing systematic errors from temperature changes and by implementing a recently described coulometric method. The electrodes are mounted in a symmetrical cell reported in part 1 of this work to reduce sensor drift and minimize inaccuracies due to liquid junction potential variations and pH changes of the inner solution from temperature fluctuations. The development and construction of the probe are explained. The circuit is evaluated, and the sensors are calibrated over a range of temperatures, approaching ideal behavior. The submersible probe was deployed in situ in April 2025 in the vertically stratified Krka River Estuary in Croatia. The precision of the probe was evaluated in situ by stability experiments in the seawater layer. The determined precision is 0.001 pH unit, which is significantly better than that reported earlier for routine pH probes. A recalibration procedure with synthetic seawater is also evaluated for minimizing drift. A depth profile with a changing salinity was performed and compared with multiparameter probes.
In this paper, we report a fully automated, end-to-end (sample-in/data-out) capillary electrophoresis system. The system's dimensions conform to the cylindrical shape and power/data requirements of the science payload compartment of the Exobiology Extant Life Surveyor (EELS), a snake-like robot capable of autonomously navigating challenging terrain on Earth or other worlds. The capillary electrophoresis system is equipped with three contactless conductivity detectors: two dedicated to analyte detection and one for characterizing bulk sample flow. The system enables simultaneous detection of cations and anions, including K+, Na+, Ca2+, Mg2+, Cl-, and SO4 2-, at submicromolar concentrations. For the first time, we deployed and demonstrated autonomous capillary electrophoresis operation on a glacier with the system tested in three environments: on-ice, partially submerged in an active stream, and fully submerged in a glacial pond.
The stable isotope ratios of carbon (δ13C-(CH4)) and hydrogen (δ2H-(CH4)) in methane (CH4) from atmospheric air samples provide a tracer that can help distinguish the relative contribution of emission sources. These can be continuously measured at atmospheric monitoring stations by optical isotope ratio spectrometer (OIRS) instruments, providing data that is complementary to isotope ratio mass spectrometry (IRMS) measurements. OIRS instruments directly measure the amount fraction of the 12CH4, 13CH4, and 12CH3 2H isotopologues, in contrast to the IRMS method of conversion to CO2 and H2, so they have different calibration needs related to reference materials (RMs) and analysis protocol. We use a single high-purity source of CH4, which has been isotopically characterized by IRMS, to produce two calibration RMs that bracket the sample in amount fraction. The isotope ratio measurement is calibrated via the isotopologue amount fraction, which is used to derive analytical expressions for the combined uncertainty. We test this approach using a 550 μmol mol-1 sample (representative of the amount fraction produced from air sampled by the NPL preconcentrator) prepared from a separate CH4 source. The combined standard uncertainty for the isotope ratio of this sample is 0.19 ‰ for δ13C-(CH4) and 1.1 ‰ for δ2H-(CH4), including uncertainty contributions from the isotopic assignment of the CH4 used for the RMs, their gravimetric preparation, and the spectrometer noise. The dominant contribution to this is from the uncertainty in isotopic assignment of the CH4 used in RM preparation. The next largest contribution to the uncertainty budget is the measurement noise, estimated from the Allan-Werle deviation, and the smallest contribution is from the preparation uncertainty in the total CH4 amount fraction in the RMs. We demonstrate that preparing the bracketing RMs from a common CH4 source results in correlations between isotopologue amount fractions and that neglecting this leads to an overestimation of the isotope ratio uncertainty.
Ammonia is a critical impurity in hydrogen fuel due to its irreversible poisoning effect on proton exchange membrane fuel cells. Therefore, international standards (e.g., ISO 14687) set a stringent threshold of 100 nmol/mol. Furthermore, with the growing potential use of ammonia as a hydrogen carrier, its accurate quantification is becoming increasingly important. However, the presence of trace humidity poses analytical challenges, as ammonia may interact with water or interfaces, thereby affecting its detectability. Therefore, the goal of this work is to enable accurate trace ammonia quantification for hydrogen purity measurements through fundamental studies of the methodological challenges. Here, low-pressure sampling (ultra)-long-path Optical-Feedback Cavity-Enhanced Absorption Spectroscopy (OF-CEAS) was applied with an effective optical path length of approximately 6.17 km. We studied three average amounts of ammonia: (38.2 ± 0.8) nmol/mol, (74.8 ± 0.7) nmol/mol, and (112.1 ± 1.2) nmol/mol. Furthermore, these amounts were investigated at trace-humidity levels ranging from 0.8 to 8.5 ppmV. We observed a systematic, nonlinear, and humidity-dependent positive measurement bias of up to + (1.0 ± 0.2) nmol/mol at the maximum investigated trace-humidity volume fraction of 8.5 ppmV. This bias was not caused by spectral interference but rather by water-induced accumulation of ammonia within the optical cavity. Moreover, time-resolved measurements in the presence of trace ammonia showed that water desorption follows first-order kinetics, whereas water adsorption followed mixed-order kinetics with an apparent reaction order of 1.57 ± 0.03. Distinct hydration states of surface-bound ammonia were identified, whereas under dry conditions and with increasing amounts of ammonia, enhanced surface adhesion through intermolecular clustering was observed. In addition, the presence of ammonium species within the sorption layer was indirectly confirmed by our experiments. In conclusion, we provide a deeper insight into trace-level ammonia-water interactions and establish a framework for optimizing methodologies, particularly for (ultra)-long-path optical gas measurement systems.
Measuring refractive index (RI) changes of liquid samples is central to many sensing applications including flow injection analysis, liquid chromatography, biosensing and photothermal spectroscopy. Commercial refractive index detectors optimized for liquid chromatography suffer from a limited linear range and measurement rate, restricting their use largely to separation sciences. In contrast, microring resonators (MRR) integrated with low-volume microfluidics, offer enhanced performance by minimizing sample dilution during flow-through RI measurements and increased dynamic range. MRRs realized by modern photonic integrated circuitry (PIC) technology also have the potential to be used as transducers in more advanced sensing schemes. Here, we demonstrate a silicon nitride (Si3N4) MRR integrated into a low-volume microfluidic system as a compact, chip-scale RI detector capable of real-time operation under dynamic flow conditions. Two interrogation modalities were experimentally compared for flow-through liquid sensing using the same MRR for the first time: resonance wavelength scanning for wide-range refractive index detection, and fixed-wavelength probing on the resonance slope for high-speed measurements. Using glucose solutions as test samples, the device was benchmarked against a commercial RI detector, achieving a sensitivity of 113 nm/RIU and a sLOD of 2.3 × 10-6 RIU (0.014 g/L glucose). To demonstrate the applicability of the developed RI-sensor for resolving transient RI peaks in realistic chromatographic flow conditions we also report its successful use in an isocratic separation of four sugars (sorbitol, fructose, glucose, and sucrose). These results highlight the potential of integrated Si3N4 MRRs as versatile, miniaturized transducers for quantitative, high-speed RI sensing in flow-based analytical systems.
Photocatalysis has been extensively studied for its potential to harness abundant sunlight energy, yet exploration has been limited by the time and effort required for performance evaluation. To screen candidate materials, including the elements across the entire periodic table, throughput must be improved while minimizing labor. In this study, we introduce a simple, labor-saving, high-throughput assay for evaluating photocatalyst activity utilizing a 96-well microplate. The protocol provides a streamlined workflow that encompasses weighing, microplate preparation, light irradiation, spectroscopic measurement, and reaction rate analysis. Importantly, this protocol removes the bottleneck of separating photocatalyst powders from the dye solution throughout the cycles of light irradiation and spectral measurements, which significantly improves the throughput and saved labor. As a foundation for this method, we investigated the relationship between the coexistence of dye and powder against the resulting apparent absorbance and the temporal profile of absorbance during the photocatalytic reaction. From the result, we provide guidelines for determining versatile amounts of the photocatalyst and dye depending on the balance between measurement accuracy and throughput. As the method relies on the additivity of absorption and scattering within a defined optical density window, it is not restricted to a particular dye. This assay enables photocatalyst performance evaluation for ∼500/day, which holds promise for exploring the vast material space across the periodic table, significantly broadening the horizons for discovering novel photocatalysts.
Chronic stress is a critical public health concern with strong links to both mental and physical health. Excessive stress alters brain architecture and stimulates the adrenal gland to release cortisol, a key biomarker of stress. Conventional cortisol measurements rely on invasive, laboratory-based methods that are unsuitable for real-time, point-of-care testing. This study reports the development of a noninvasive, flexible electrochemical biosensor for the selective detection of cortisol in human sweat and saliva. The sensor was fabricated on a polyethylene terephthalate (PET) substrate coated with indium tin oxide (ITO) and functionalized with gold nanoparticles (AuNPs) to enhance conductivity and surface area. AnteoBind chemistry was employed for oriented immobilization of monoclonal anticortisol antibodies. The device was integrated with a microfluidic platform to enable controlled sweat analysis. Electrochemical measurements demonstrated a linear response over the range of 0.5-150 ng/mL, with a detection limit of 0.58 ng/mL. The sensor retained 80% of its signal over 30 days, with reproducibility across batches (RSD ∼ 3.2%). Real sample analysis showed recovery values of 95.5-104% in sweat and saliva samples. These findings underscore the potential of this biosensor for noninvasive monitoring of stress markers in wearable applications, supporting early stress detection and personalized health management.
Membrane structures and cellular phenomena have been studied using scanning ion conductance microscopy (SICM). Conventional techniques for studying single cells, such as optical microscopy, fluorescence microscopy, electron microscopy (EM), and atomic force microscopy (AFM), have provided a wealth of information on the architecture of cell membranes, but they could potentially be invasive to live cells due to reasons including phototoxicity, electron beam damage, and cantilever-mediated damage to the cell membrane. While super-resolution approaches such as stimulated emission depletion (STED) microscopy have extended the capabilities of optical imaging, conventional optical microscopy remains limited by the diffraction limit in resolving intricate structures on cell membranes. In this review article, we discuss SICM as a technique that allows noninvasive imaging of live single cells in aqueous solutions, including cell culture media. We also discuss the fabrication and characterization of nanopipettes, advances in instrumentation and scanning regimes used in SICM, and applications of nanopipettes in the technique for topography mapping, high spatial resolution imaging, precise delivery of molecules to cells, biopsy, and surface charge measurements. We also discuss how nanopipettes are functionalized for applications in the simultaneous mapping of cell topography and high spatial resolution sensing, such as extracellular pH mapping. SICM has also been combined with scanning electrochemical microscopy (SECM) to enable the measurement of electroactive species at the cell membrane, and applied in cell surface charge mapping, where membrane charge is implicated in many cellular events. Advances in SICM imaging speed will allow the capture of fast cellular phenomena, and because the application of nanopipettes in SICM for high spatial resolution topographic imaging and sensing is still in its infancy, these developments could open new opportunities for imaging the distribution of analytes around live single cells.
Quantitative assessment of nanoparticle dispersion in dilute, realistic solvent environments remains a critical challenge for materials intended for use in complex ionic systems, such as carbon dioxide underground and storage (CCS) and enhanced oil recovery (EOR) technologies. While conventional techniques, including dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS), provide valuable ensemble-averaged information, they are particularly limited in dilute systems and lack the spatial resolution required to characterize local dispersion heterogeneities and microscopic agglomeration behavior. Here, we present a quantitative analytical framework that combines cryogenic transmission electron microscopy (cryo-TEM) with AI-integrated automated imaging and Voronoi tessellation analysis to directly visualize and quantify silica nanoparticle dispersion states in saline solutions. Silica nanoparticles functionalized with various organic acidsincluding malonic, succinic, maleic, DL-malic, and citric acids, as well as L-arabinosewere prepared and examined using cryo-TEM. For each frozen-hydrated sample, several hundred images were acquired under standardized conditions. Automated particle identification and subsequent Voronoi tessellation yielded quantitative dispersion parameters. The coefficient of variation (CV) of Voronoi region areas was introduced as a dimensionless metric to enable intersample comparison. The results revealed distinct dispersion behaviors: samples modified with maleic, DL-malic, and citric acids demonstrated high dispersion stability (CV ≈ 0.4), while unmodified and L-arabinose-modified samples exhibited pronounced agglomeration tendencies (CV ≈ 0.8). Notably, the CV values correlated strongly with DLS-measured particle diameters, further validating the reliability of the proposed methodology. This approach advances the field from qualitative cryo-TEM observation toward quantitative materials characterization, providing mechanistic insights into the effects of surface modification on dispersion stability at the microscopic scale. Furthermore, it offers a robust platform for evaluating nanoparticle behavior under practically relevant solvent conditions.
Accurate pH measurement in microliter volumes is essential for real-time chemical and biological analysis, underpinning modern portable and point-of-care diagnostics. Here, we present a wireless electrochemical pH sensor system based on nanostructured iridium oxide (IrO2) electrodeposited onto electron-beam-evaporated, photolithographically patterned gold electrodes (EBLG) and integrated with a low-power Bluetooth potentiostat for continuous, real-time pH monitoring. FESEM, Raman, and XPS confirm uniform spherical IrO2 nanostructures with mixed Ir3+/Ir4+ states that are conducive to potentiometric transduction. The IrO2/EBLG sensor exhibited near-Nernstian sensitivity (69.7 mV/pH) across a pH range of 2-9, a fast step response (∼10 s), negligible hysteresis during bidirectional cycling, and exceptionally low potential drift (∼0.12-0.28 mV/h over 12 h). It demonstrates high selectivity toward H+ ions against common physiological interferences with excellent reproducibility and robust long-term stability. The hand-held module wirelessly streams real-time potential data to a smartphone, enabling accurate pH quantification in microliter-scale biological, food, and environmental samples. Measurements showed strong agreement with a commercial microelectrode pH meter, with no statistically significant difference (p > 0.05; Bland-Altman and paired t-test). Overall, the IrO2/EBLG platform combines miniaturization, stability, and wireless functionality, offering a reliable and scalable solution for decentralized pH sensing and paving a promising route toward future wearable, field-deployable, and environmental pH monitoring systems.
Accurate determination of optical bandgaps is essential for understanding and designing materials used in photovoltaics, photocatalysis, light-emitting devices, and quantum technologies. Yet, the most widely employed methods, such as Tauc-plot and photoelectron spectroscopy extrapolation, remain inherently subjective, relying on user-defined fitting regions and/or assumptions about transition types. Here we demonstrate that on-resonance fluorescence (ORF), a universal photophysical process occurring where absorption and emission spectra overlap, provides a direct, objective, and experimentally accessible measure of bandgap energy in fluorescent materials. We show both theoretically and experimentally that the ORF peak wavelength corresponds to the electronic bandgap, independent of scattering, absorption tails, or operator bias. In contrast to traditional methods that are valid only for single-bandgap systems and yield erroneous results for materials containing multiple emissive components, the ORF approach uniquely resolves the individual bandgaps of multicomponent or heterojunction samples without sample separation or prior assumptions. Using small-molecule fluorophores, quantum dots, and OLED dopants as model systems, we establish ORF as a broadly applicable, high-throughput, and reproducible approach for bandgap evaluation with experimental accuracy within ± 0.01 eV. Another key breakthrough capability is the determination of the individual bandgaps in samples containing two fluorescence materials. Beyond its simplicity, this method reframes ORF as a quantitative spectroscopic marker of bandgaps, offering a paradigm-shifting, broadly accessible alternative to Tauc-based analyses for both research and chemical education.
Detection of waterborne pathogens benefits from measurement strategies that combine sensitivity with a practical workflow. We report a side-by-side metrological comparison of two orthogonal readouts measured on the same immunomagnetically captured cells using bifunctional Fe3O4 nanoparticles (MNPs) conjugated to anti-E. coli IgG and ferrocene (Fc). Under our protocol, these Fc-IgG MNPs achieved capturing efficiency up to 95% for Escherichia coli (E. coli) K12. The electrochemical readout, performed by a microfabricated chip (via differential pulse voltammetry of Fc) shows concentration-dependent signal suppression upon capture of E. coli K12, with an apparent detection limit as low as 10 cells·mL-1 and a broad dynamic range spanning 101 to 109 cells·mL-1. A complementary on-chip fluorescence readout (via Nile Red staining) provides visual corroboration and specificity support but exhibits reduced analytical sensitivity, approximately 4 orders of magnitude lower than the electrochemical approach, consistent with nanoparticle-induced quenching at lower cell counts. Across model and drinking-water matrices tested, the comparative performance trend is preserved, and the complete workflow can be completed in about 2 h from capture to readout. Taken together, these results present a comprehensive, internally consistent comparison on a single capture construct and clarify when electrochemistry versus fluorescence is fit-for-purpose in the context of immunomagnetic separation. The platform supports low-consumable testing with a reusable, unmodified electrochemical chip, is amenable to multiplexed array formats, and is readily compatible with portable potentiostats for on-site measurements.
Sensitive and selective detection of neurochemicals such as neuropeptides is critical for understanding brain signaling. While carbon-fiber microelectrodes (CFMEs) are widely used for these measurements, alternative electrode materials and fabrication techniques could improve sensitivity and versatility. In this study, we investigate pyrolyzed parylene-N microelectrodes (PPNMEs) as a promising platform for making thin-film carbon electrodes for the detection of electroactive amino acids and neuropeptides. We evaluated the performance of PPNMEs for the detection of tryptophan (Trp), tyrosine (Tyr), and the neuropeptide gonadotropin-releasing hormone (GnRH), which contains these electroactive residues. PPNMEs demonstrated significantly greater sensitivity with fast-scan cyclic voltammetry, with signal amplitudes approximately four times higher than those observed with CFMEs. After normalization for surface area, PPNMEs exhibited 3-, 5-, and 2.7-fold higher signals than CFMEs for Trp, Tyr, and GnRH, respectively. Additionally, PPNMEs facilitated faster electron transfer kinetics, as evidenced by reduced oxidation potentials. There were enhanced signals for secondary oxidation peaks at PPNMEs because the rougher surface can trap intermediates near the surface, facilitating detection of downstream electrochemical reactions. Scan rate analysis indicates more adsorption-controlled detection, contributing to improved sensitivity. Importantly, PPNMEs enabled sensitive detection of GnRH in brain tissue slices, including both puffed-on applications and spontaneous endogenous GnRH release in the median eminence. These results highlight the potential of PPNMEs as a new class of carbon-based electrodes, offering a promising alternative to CFMEs for high-sensitivity, low-potential detection of neurochemicals in biological tissues.
In order to achieve optimum conditions of electrospray ionization (ESI) mass spectrometry (MS) methods, samples and mobile phases are often supplemented with acids, bases, supercharging reagents, and electrospray-friendly solvents. Typically, one pH or concentration of these additives is used in a given method, which is selected based on iterative optimization or literature. However, different pH values and concentrations of additives can be suitable for the analysis of different species, and subtle changes can bring different analytical information. Therefore, here we demonstrate a precise MS optimization system enabling dynamic scans of acid-base and additive concentrations in ESI-MS. In the case of low-molecular-weight analytes, MS signals can be enhanced by selecting the optimum conditions in single scans. The online acid-base scan showed enhancement factors of ∼5.4-7.9 for amino acids and related compounds, ∼44.7 for glutathione, and ∼4.5-10.3 for some tested phospholipids at 25%, 75%, and 90% base (stock solution volume ratio), respectively. For proteins, charge state distributions (CSDs) can be manipulated, bringing information on vulnerability of the protein tertiary structures to the changing environment. Multiple charging of cytochrome c and myoglobin was enhanced to varying degrees upon increasing concentrations of sulfolane and dimethyl sulfoxide, while increasing concentrations of organic solvents shifted CSDs to lower charge states. The setup for such measurements was constructed by using off-the-shelf components and by taking advantage of a simple Python code. Coupling online additive scans with ESI-MS streamlines optimization by eliminating the need for multiple sequential analyses of additives used to enhance signal intensity or induce supercharging.