Development of a Low-Cost Paper-Based Platform for Coffee Ring-Assisted SERS

PySpectro: A modular 3D printed, machine learning assisted optical device for recognition and quantification of samples

Over the past decade, science and technology have achieved great advancements driven by the synergy between materials and manufacturing processes; coupled with the growth of informatics, which offers powerful tools to process and interpret data, new analytical devices have been developed. This work describes a modular 3D printed instrument that utilizes the AS7262 light sensor coupled with a LED to perform absorbance and reflectance measurements. The mode of operation can be switched by conveniently attaching different 3D printed parts. An Arduino Nano is used for operating the electronics, and a python-based software is employed for data handling. The device, beside spectra acquisition, allows rapid identification and quantification of samples through a database and machine learning (ML) algorithms. A recursive methodology for regression specifically designed allowed sample quantification in a range spanning around 2.5 orders of magnitude with errors generally below 10%. PySpectro was used on homogeneous solution and on PADs (Paper-based Analytical Devices) for food dyes and phosphomolybdic assay for phosphate. The device may find applications in any colorimetric detection also outside the laboratory environment and can be a time-saving tool for fast preliminary determinations or educational purposes.

Plasmonic coffee-ring biosensing for AI-assisted point-of-care diagnostics

A major challenge in addressing global health issues is developing simple, affordable biosensors with high sensitivity and specificity. Significant progress has been made in at-home medical detection kits, especially during the COVID-19 pandemic. Here, we demonstrated a coffee-ring biosensor with ultrahigh sensitivity, utilizing the evaporation of two sessile droplets and the formation of coffee-rings with asymmetric nanoplasmonic patterns to detect disease-relevant proteins as low as 3 pg/ml, under 12 min. Experimentally, a protein-laden droplet dries on a nanofibrous membrane, pre-concentrating biomarkers at the coffee ring. A second plasmonic droplet with functionalized gold nanoshells is then deposited at an overlapping spot and dried, forming a visible asymmetric plasmonic pattern due to distinct aggregation mechanisms. To enhance detection sensitivity, a deep neural model integrating generative and convolutional networks was used to enable quantitative biomarker diagnosis from smartphone photos. We tested four different proteins, Procalcitonin (PCT) for sepsis, SARS-CoV-2 Nucleocapsid (N) protein for COVID-19, Carcinoembryonic antigen (CEA) and Prostate-specific antigen (PSA) for cancer diagnosis, showing a working concentration range over five orders of magnitude. Sensitivities surpass equivalent lateral flow immunoassays by over two orders of magnitude using human saliva samples. The detection principle, along with the device, and materials can be further advanced for early disease diagnostics.

Rapid and sensitive detection of pharmaceutical pollutants in aquaculture by aluminum foil substrate based SERS method combined with deep learning algorithm

BACKGROUND

Pharmaceutical residual such as antibiotics and disinfectants in aquaculture wastewater have significant potential risks for environment and human health. Surface enhanced Raman spectroscopy (SERS) has been widely used for the detection of pharmaceuticals due to its high sensitivity, low cost, and rapidity. However, it is remain a challenge for high-sensitivity SERS detection and accurate identification of complex pollutants.

RESULTS

Hence, in this work, we developed an aluminum foil (AlF) based SERS detection substrate and established a multilayer perceptron (MLP) deep learning model for the rapid identification of antibiotic components in a mixture. The detection method demonstrated exceptional performance, achieving a high SERS enhancement factor of 4.2 × 105 and excellent sensitivity for trace amounts of fleroxacin (2.7 × 10-8 mol/L), levofloxacin (1.95 × 10-8 mol/L), and pefloxacin (6.9 × 10-8 mol/L),sulfadiazine, methylene blue, and malachite green at a concentration of 1 × 10-8 mol/L can all be detected, the concentrations of the six target compounds and their Raman intensities exhibit a good linear relationship. Moreover, the AlF SERS substrate can be prepared rapidly without adding organic reagents, and it exhibited good reproducibility, with RSD<9.6 %. Additionally, the algorithm model can accurately identify the contaminants mixture of sulfadiazine, methylene blue, and malachite green with a recognition accuracy of 97.8 %, an F1-score of 98.2 %, and a 5-fold cross validation score of 97.4 %, the interpretation analysis using Shapley Additive Explanations (SHAP) reveals that MLP model can specifically concentrate on the distribution of characteristic peaks.

SIGNIFICANCE

The experimental results indicated that the MLP model demonstrated strong performance and good robustness in complex matrices. This research provides a promising detection and identification method for the antibiotics and disinfectants in actual aquaculture wastewater treatment.

Point-of-need diagnostics in a post-Covid world: an opportunity for paper-based microfluidics to serve during syndemics

Zoonotic outbreaks present with unpredictable threats to human health, food production, biodiversity, national security and disrupt the global economy. The COVID-19 pandemic-caused by zoonotic coronavirus, SARS-CoV2- is the most recent upsurge of an increasing trend in outbreaks for the past 100 years. This year, emergence of avian influenza (H5N1) is a stark reminder of the need for national and international pandemic preparedness. Tools for threat reduction include consistent practices in reporting pandemics, and widespread availability of accurate detection technologies. Wars and extreme climate events redouble the need for fast, adaptable and affordable diagnostics at the point of need. During the recent pandemic, rapid home tests for SARS-CoV-2 proved to be a viable functional model that leverages simplicity. In this perspective, we introduce the concept of syndemnicity in the context of infectious diseases and point-of-need healthcare diagnostics. We also provide a brief state-of-the-art for paper-based microfluidics. We illustrate our arguments with a case study for detecting brucellosis in cows. Finally, we conclude with lessons learned, challenges and opportunities for paper-based microfluidics to serve point-of-need healthcare diagnostics during syndemics.

Crystal Patterning from Aqueous Solutions via Solutal Instabilities

Fluid instabilities can be harnessed for facile self-assembly of patterned structures on the nano- and microscale. Evaporative self-assembly from drops is one simple technique that enables a range of patterning behaviors due to the multitude of fluid instabilities that arise due to the simultaneous existence of temperature and solutal gradients. However, the method suffers from limited controllability over patterns that can arise and their morphology. Here, we demonstrate that a range of distinct crystalline patterns including hexagonal arrays, branches, and sawtooth structures emerge from evaporation of water drops containing calcium sulfate on hydrophilic and superhydrophilic substrates. Different pattern regimes emerge as a function of contact line dynamics and evaporation rates, which dictate which fluid instabilities are most likely to emerge. The underlying physical mechanisms behind instability for controlled self-assembly involve Marangoni flows and forced wetting/dewetting. We also demonstrate that these patterns composed of water-soluble inorganic crystals can serve as sustainable and easily removable masks for applications in microscale fabrication.

Superhydrophobic nanocellulose-based self-assembled flexible SERS substrates for pesticide detection

Flexible surface-enhanced Raman scattering (SERS) substrates that provide simple sampling are helpful for the on-site detection of explosive contamination, pesticide residues on food surfaces, and water pollution in public spaces. Using superhydrophobic nanocellulose-based film as the support, 2D flexible SERS substrates that integrated sampling, enrichment, and detection were successfully fabricated via the solvent-induced evaporation method. This approach enabled the co-loading of two plasmonic nanoparticles with different sizes and shapes. A uniform and dense distribution of two-dimensional "hot spots" was created by the plasmonic nanoparticles' self-assembly on the hydrophobic substrate. By adjusting the loading ratio of Au-core/Ag-shell nanocubes and gold nanospheres, their synergistic effect optimized the "hot spots" structure and significantly increased the SERS signal intensity. Additionally, the hydrophobic property of the substrate allowed the target analytes to be concentrated throughout the drying process, significantly increasing the sensitivity of SERS detection. This flexible substrate can sensitively and accurately detect the pesticide residues of phosphorus and methyl parathion on apple peel with the detection limit of 10-7 g/L and relative standard deviation (RSD) less than 10 %. The high-performance SERS substrate has great potential for in-situ detection applications such as food safety and environmental monitoring.

An ultrasensitive paper-based SERS sensor for detection of nucleolin using silver-nanostars, plastic antibodies and natural antibodies

A state-of-the-art, ultrasensitive, paper-based SERS sensor has been developed using silver nanostars (AgNSs) in combination with synthetic and natural antibodies. A key component of this innovative sensor is the plastic antibody, which was synthesized using molecularly imprinted polymer (MIP) technology. This ground-breaking combination of paper substrates/MIPs with AgNSs, which is similar to a sandwich immunoassay, is used for the first time with the aim of SERS detection and specifically targets nucleolin (NCL), a cancer biomarker. The sensor device was carefully fabricated by synthesizing a polyacrylamide-based MIP on cellulose paper (Whatman Grade 1 filter) by photopolymerization. The binding of NCL to the MIP was then confirmed by natural antibody binding using a sandwich assay for quantitative SERS analysis. To facilitate the detection of NCL, antibodies were pre-bound to AgNSs with a Raman tag so that the SERS signal could indicate the presence of NCL. The composition of the sensory layers/materials was meticulously optimized. The intensity of the Raman signal at ∼1078 cm-1 showed a linear trend that correlated with increasing concentrations of NCL, ranging from 0.1 to 1000 nmol L-1, with a limit of detection down to 0.068 nmol L-1 in human serum. The selectivity of the sensor was confirmed by testing its analytical response in the presence of cystatin C and lysozyme. The paper-based SERS detection system for NCL is characterized by its simplicity, sustainability, high sensitivity and stability and thus embodies essential properties for point-of-care applications. This approach is promising for expansion to other biomarkers in various fields, depending on the availability of synthetic and natural antibodies.

Regenerated SERS substrate based on Ag/AuNPs-TiO2-oxidized carbon cloth for detection of imidacloprid

Imidacloprid (IMI) are widely used in modern tea industry for pest control, but IMI residues pose a great threat to human health. Herein, we propose a regeneration metal-semiconductor SERS substrate for IMI detection. We fabricated the SERS sensor through the in-situ growth of a nano-heterostructure incorporating a semiconductor (TiO2) and plasmonic metals (Au, Ag) on oxidized carbon cloth (OCC). Leveraging the high-density hot spots, the formed Ag/AuNPs-TiO2-OCC substrate exhibits higher enhancement factors (1.92 × 108) and uniformity (RSD = 7.68%). As for the detection of IMI on the substrate, the limit of detection was lowered to 4.1 × 10-6 μg/mL. With a hydrophobic structure, the Ag/AuNPs-TiO2-OCC possessed excellent self-cleaning performance addressing the limitation of single-use associated with traditional SERS substrates, as well as the degradation capability of the substrate under ultraviolet (UV) light. Accordingly, Ag/AuNPs-TiO2-OCC showcases outstanding SERS sensing and regenerating properties, making it poised for extensive application in the field of food safety assurance.

Fundamentals and Applications of Surface Wetting

In an era defined by an insatiable thirst for sustainable energy solutions, responsible water management, and cutting-edge lab-on-a-chip diagnostics, surface wettability plays a pivotal role in these fields. The seamless integration of fundamental research and the following demonstration of applications on these groundbreaking technologies hinges on manipulating fluid through surface wettability, significantly optimizing performance, enhancing efficiency, and advancing overall sustainability. This Review explores the behavior of liquids when they engage with engineered surfaces, delving into the far-reaching implications of these interactions in various applications. Specifically, we explore surface wetting, dissecting it into three distinctive facets. First, we delve into the fundamental principles that underpin surface wetting. Next, we navigate the intricate liquid-surface interactions, unraveling the complex interplay of various fluid dynamics, as well as heat- and mass-transport mechanisms. Finally, we report on the practical realm, where we scrutinize the myriad applications of these principles in everyday processes and real-world scenarios.