A deep transfer learning-based protocol accelerates full quantum mechanics calculation of protein.

Effective full quantum mechanics (FQM) calculation of protein remains a grand challenge and of great interest in computational biology with substantial applications in drug discovery, protein dynamic simulation and protein folding. However, the huge computational complexity of the existing QM methods impends their applications in large systems. Here, we design a transfer-learning-based deep learning (TDL) protocol for effective FQM calculations (TDL-FQM) on proteins. By incorporating a transfer-learning algorithm into deep neural network (DNN), the TDL-FQM protocol is capable of performing calculations at any given accuracy using models trained from small datasets with high-precision and knowledge learned from large amount of low-level calculations. The high-level double-hybrid DFT functional and high-level quality of basis set is used in this work as a case study to evaluate the performance of TDL-FQM, where the selected 15 proteins are predicted to have a mean absolute error of 0.01 kcal/mol/atom for potential energy and an average root mean square error of 1.47 kcal/mol/$ {\rm A^{^{ \!\!\!o}}} $ for atomic forces. The proposed TDL-FQM approach accelerates the FQM calculation more than thirty thousand times faster in average and presents more significant benefits in efficiency as the size of protein increases. The ability to learn knowledge from one task to solve related problems demonstrates that the proposed TDL-FQM overcomes the limitation of standard DNN and has a strong power to predict proteins with high precision, which solves the challenge of high precision prediction in large chemical and biological systems.

Machine Learning-Assisted Portable Microplasma Optical Emission Spectrometer for Food Safety Monitoring.

To meet the needs of food safety for simple, rapid, and low-cost analytical methods, a portable device based on a point discharge microplasma optical emission spectrometer (µPD-OES) was combined with machine learning to enable on-site food freshness evaluation and detection of adulteration. The device was integrated with two modular injection units (i.e., headspace solid-phase microextraction and headspace purge) for the examination of various samples. Aromas from meat and coffee were first introduced to the portable device. The aroma molecules were excited to specific atomic and molecular fragments at excited states by room temperature and atmospheric pressure microplasma due to their different atoms and molecular structures. Subsequently, different aromatic molecules obtained their own specific molecular and atomic emission spectra. With the help of machine learning, the portable device was successfully applied to the assessment of meat freshness with accuracies of 96.0, 98.7, and 94.7% for beef, pork, and chicken meat, respectively, through optical emission patterns of the aroma at different storage times. Furthermore, the developed procedures can identify beef samples containing different amounts of duck meat with an accuracy of 99.5% and classify two coffee species without errors, demonstrating the great potential of their application in the discrimination of food adulteration. The combination of machine learning and µPD-OES provides a simple, portable, and cost-effective strategy for food aroma analysis, potentially addressing field monitoring of food safety.

Field Detection of Uranyl in Coastal Water of China Using a Portable Device via DNA Photocleavage.

The urgent need for field detection of uranium in seawater is 2-fold: to provide prompt guidance for uranium extraction and to prevent human exposure to nuclear radiation. However, current methods for this purpose are largely hindered by bulky instrumentation, high costs of developed materials, and severe matrix interferences, which limit their further application in the field. Herein, we demonstrated a portable and label-free strategy for the field detection of uranyl in seawater based on the efficient photocleavage of DNA. Further experiments confirmed the generation of ultraviolet (UV) light-induced reactive oxygen species (ROS), such as O2•- and •OH, which fragmented oligomeric DNA in the presence of uranyl and UV light. Detailed studies showed that DNA significantly enhances uranyl absorption in the UV-visible region, leading to the generation of more ROS. A fluorescence system for the selective detection of uranyl in seawater was established by immobilizing two complementary oligonucleotides with the fluorescent dye SYBR Green I. The strategy of UV-induced photocleavage offers high selectivity, excellent interference immunity, and high sensitivity for uranyl, with a detection limit of 6.8 nM. Additionally, the fluorescence can be visually detected using a 3D-printed miniaturized device integrated with a smartphone. This method has been successfully applied to the on-site detection of uranyl in seawater in 18 Chinese coastal cities and along the coast of Hainan Island within 3 min for a single sample. The sample testing and field analysis results indicate that this strategy has promising potential for real-time monitoring of trace uranyl in China's coastal waters. It is expected to be utilized for the rapid assessment of nuclear contamination and nuclear engineering construction.

An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds.

Accurate simulation of protein folding is a unique challenge in understanding the physical process of protein folding, with important implications for protein design and drug discovery. Molecular dynamics simulation strongly requires advanced force fields with high accuracy to achieve correct folding. However, the current force fields are inaccurate, inapplicable and inefficient. We propose a machine learning protocol, the inductive transfer learning force field (ITLFF), to construct protein force fields in seconds with any level of accuracy from a small dataset. This process is achieved by incorporating an inductive transfer learning algorithm into deep neural networks, which learn knowledge of any high-level calculations from a large dataset of low-level method. Here, we use a double-hybrid density functional theory (DFT) as a case functional, but ITLFF is suitable for any high-precision functional. The performance of the selected 18 proteins indicates that compared with the fragment-based double-hybrid DFT algorithm, the force field constructed by ITLFF achieves considerable accuracy with a mean absolute error of 0.0039 kcal/mol/atom for energy and a root mean square error of 2.57 $\mathrm{kcal}/\mathrm{mol}/{\AA}$ for force, and it is more than 30 000 times faster and obtains more significant efficiency benefits as the system increases. The outstanding performance of ITLFF provides promising prospects for accurate and efficient protein dynamic simulations and makes an important step toward protein folding simulation. Due to the ability of ITLFF to utilize the knowledge acquired in one task to solve related problems, it is also applicable for various problems in biology, chemistry and material science.

Point-of-Care Platform Based on Solid-Phase Fluorescence Filter Effect for Urinary Iodine Testing in Children and Pregnant Women.

Iodine is an essential element that is used to make thyroid hormones. However, people usually ignore their iodine nutrition level, thus leading to a series of thyroid diseases, particularly in areas where medical resources are scarce. Thus, development of a portable, economical, and simple method for the detection of urinary iodine is of significant importance. Herein, a solid-phase fluorescence filter effect (SPFFE) induced by iodine was used to develop an SPFFE-based point-of-care testing (POCT) platform for the detection of urinary iodine by coupling with headspace sample introduction. This method can not only alleviate the matrix interference that occurred in the conventional inner filter effect (IFE) but also achieve high sensitivity. Furthermore, the urinary iodine (UI) POCT platform was developed through the integration of a sample pretreatment and fluorescence readout. This whole system costs less than US $20 and provides accurate temperature control and a portable fluorescence reading within 15-20 min. Compared to the traditional IFE-based assay, the SPFFE-based POCT platform allows the selective detection of iodine as low as 10 nM and has a linear range of 0.05-4 µM. In addition, it provides notable visualization from blue-violet to orange-red in the presence of iodine, which tends to indicate the iodine nutritional status of the human body. Eventually, the clinical applicability and feasibility of the UIPOCT platform as an early diagnostic test kit were confirmed by determining the iodine in urine samples from children and pregnant women.

Rapid Testing of Δ9-Tetrahydrocannabinol and Its Metabolite On-Site Using a Label-Free Ratiometric Fluorescence Assay on a Smartphone.

Excessive consumption of Δ9-tetrahydrocannabinol (THC) severely endangers human health and has raised public safety concerns. However, its quantification by readily rapid tools with simplicity and low cost is still challenging. Herein, we found that a G-rich THC aptamer (THC1.2) can tightly bind to thioflavin T (ThT) with strong fluorescence, which would be specifically quenched in the presence of THC. Based on that, a label-free ratiometric fluorescent sensor for the sensing of THC and its metabolite (THC-COOH) based on THC1.2/ThT as a color emitter and red CdTe quantum dots as reference fluorescence was constructed. Notably, a transition of the fluorescent color of the ratiometric probe from green to red can be instantly observed upon the increased concentration of THC and THC-COOH. Furthermore, a portable smartphone-based fluorescence device integrated with a self-programmed Python program was fabricated and used to accomplish on-site monitoring of THC and THC-COOH within 5 min. Under optimized conditions, this ratiometric fluorescent sensor allowed for an instant response toward THC and its metabolite with considerable limits of detection of 97 and 254 nM, respectively. The established sensor has been successfully applied to urine and saliva samples and exhibited satisfactory recoveries (88-116%). This ratiometric fluorescent sensor can be used for the simultaneous detection of THC and THC-COOH with the advantages of rapidness, low cost, ease of operation, and portability, providing a promising strategy for on-site detection and facilitating law enforcement regulation and roadside control of THC.

ChK1 activation induces reactive astrogliosis through CIP2A/PP2A/STAT3 pathway in Alzheimer's disease.

Cancerous Inhibitor of PP2A (CIP2A), an endogenous PP2A inhibitor, is upregulated and causes reactive astrogliosis, synaptic degeneration, and cognitive deficits in Alzheimer's disease (AD). However, the mechanism underlying the increased CIP2A expression in AD brains remains unclear. We here demonstrated that the DNA damage-related Checkpoint kinase 1 (ChK1) is activated in AD human brains and 3xTg-AD mice. ChK1-mediated CIP2A overexpression drives inhibition of PP2A and activates STAT3, then leads to reactive astrogliosis and neurodegeneration in vitro. Infection of mouse brain with GFAP-ChK1-AAV induced AD-like cognitive deficits and exacerbated AD pathologies in vivo. In conclusion, we showed that ChK1 activation induces reactive astrogliosis, degeneration of neurons, and exacerbation of AD through the CIP2A-PP2A-STAT3 pathway, and inhibiting ChK1 may be a potential therapeutic approach for AD treatment.

Single atom modified two-dimensional bismuthenes for toxic gas detection.

Accurate detection of toxic gases at low concentrations is often difficult because they are colorless, odorless, flammable and denser than air. Therefore, it is urgent to develop highly stable and sensitive toxic gas detectors. However, most gas sensors operate at high temperatures, making the detection of toxic gases more challenging. Two-dimensional materials with high specific surface area and abundant modulation methods of properties provide new inspirations for the development of new toxic gas sensing materials. Here, bismuthene, a single element two-dimensional material with high carrier mobility and excellent stability, was used as a substrate material to investigate the effects of anchoring and doping on its gas detection performance by density functional theory (DFT) calculations. It is revealed that the surface structure altered by single metal atoms (Ba, Be, Ca, K, Li, Mg, Na, and Sr) can promote the improvement of gas detection sensitivity. Buckled honeycomb bismuthene (bBi) with the Be atom anchored (A-Be-Bi) show superior sensitivity to H2S, while D-Ca-Bi, D-Li-Bi, D-Mg-Bi and D-Sr-Bi also have relatively high toxic gas detection sensitivity. We further discussed the recovery times of these modified bBis at various temperatures to determine the potential for applications. The ultra-fast recovery time of less than 0.5 seconds demonstrates the potential of these systems at room temperature and can be applied to the manufacture of toxic gas sensors used under practical sensing conditions.

Binding Affinity and Mechanisms of Potential Antidepressants Targeting Human NMDA Receptors.

Depression, a mental disorder that plagues the world, is a burden on many families. There is a great need for new, fast-acting antidepressants to be developed. N-methyl-D-aspartic acid (NMDA) is an ionotropic glutamate receptor that plays an important role in learning and memory processes and its TMD region is considered as a potential target to treat depression. However, due to the unclear binding sites and pathways, the mechanism of drug binding lacks basic explanation, which brings great complexity to the development of new drugs. In this study, we investigated the binding affinity and mechanisms of an FDA-approved antidepressant (S-ketamine) and seven potential antidepressants (R-ketamine, memantine, lanicemine, dextromethorphan, Ro 25-6981, ifenprodil, and traxoprodil) targeting the NMDA receptor by ligand-protein docking and molecular dynamics simulations. The results indicated that Ro 25-6981 has the strongest binding affinity to the TMD region of the NMDA receptor among the eight selected drugs, suggesting its potential effective inhibitory effect. We also calculated the critical binding-site residues at the active site and found that residues Leu124 and Met63 contributed the most to the binding energy by decomposing the free energy contributions on a per-residue basis. We further compared S-ketamine and its chiral molecule, R-ketamine, and found that R-ketamine had a stronger binding capacity to the NMDA receptor. This study provides a computational reference for the treatment of depression targeting NMDA receptors, and the proposed results will provide potential strategies for further antidepressant development and is a useful resource for the future discovery of fast-acting antidepressant candidates.

Urinary metabolomics identified metabolic disturbance associated with polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder. Nevertheless, its accurate mechanisms remain unclear. Metabolomics is a powerful technique to identify small molecules that could be used to discover pathogenesis and therapeutical targets of disease. In the present study, a urinary untargeted metabolomics combined with targeted quantification analysis was performed to uncover metabolic disturbance associated with PCOS. A total of thirty-eight metabolites were obtained between PCOS patients and healthy controls, which were mainly involved in lipids (39.5%), organic acids and derivatives (23.7%), and organic oxygen compounds (18.4%). Based on enrichment analysis, fourteen metabolic pathways were found to be perturbed in PCOS, particularly glycerophospholipid metabolism and tryptophan metabolism. Targeted quantification profiling of tryptophan metabolism demonstrated that seven compounds (tryptophan, kynurenine, kynurenic acid, quinolinic acid, xanthurenic acid, 3-hydroxyanthranilic acid and 3-hydroxykynurenine) were up-regulated in PCOS. And these tryptophan-kynurenine metabolites showed significant correlations with PCOS clinical features, such as positively associated with testosterone, free androgen index, and the ratio of luteinizing hormone to follicle stimulating hormone. Thus, this study disclosed urinary metabolome changes associated with PCOS, and might provide new insights into PCOS pathogenesis elucidation and therapeutical target development.

Polyphyllin I attenuates cognitive impairments and reduces AD-like pathology through CIP2A-PP2A signaling pathway in 3XTg-AD mice.

Polyphyllin I (PPI) is a natural phytochemical drug isolated from plants which can inhibit the proliferation of cancer cells. One of the PPI tumor-inhibitory effects is through downregulating the expression of Cancerous Inhibitor of PP2A (CIP2A), the latter, is found upregulated in Alzheimer's disease (AD) brains and participates in the development of AD. In this study, we explored the application of PPI in experimental AD treatment in CIP2A-overexpressed cells and 3XTg-AD mice. In CIP2A-overexpressed HEK293 cells or primary neurons, PPI effectively reduced CIP2A level, activated PP2A, and decreased the phosphorylation of tau/APP and the level of Aß. Furthermore, synaptic protein levels were restored by PPI in primary neurons overexpressing CIP2A. Animal experiments in 3XTg-AD mice revealed that PPI treatment resulted in decreased CIP2A expression and PP2A re-activation. With the modification of CIP2A-PP2A signaling, the hyperphosphorylation of tau/APP and Aß overproduction were prevented, and the cognitive impairments of 3XTg-AD mice were rescued. In summary, PPI ameliorated AD-like pathology and cognitive impairment through modulating CIP2A-PP2A signaling pathway. It may be a potential drug candidate for the treatment of AD.

Constraint Dependence of Active Depletion Forces on Passive Particles.

Using simulations and experiments, we demonstrate that the effective interaction between passive particles in an active bath substantially depends on an external constraint suffered by the passive particles. Particularly, the effective interaction between two free passive particles, which is directly measured in simulation, is qualitatively different from the one between two fixed particles. Moreover, we find that the friction experienced by the passive particles-a kinematic constraint-similarly influences the effective interaction. These remarkable features are in significant contrast to the equilibrium cases, and mainly arise from the accumulation of the active particles near the concave gap formed by the passive spheres. This constraint dependence not only deepens our understanding of the "active depletion force," but also provides an additional tool to tune the effective interactions in an active bath.

Active noise experienced by a passive particle trapped in an active bath.

We study the properties of active noise experienced by a passive particle harmonically trapped in an active bath. The active bath is either explicitly simulated by an ensemble of active Brownian particles or abstractly represented by an active colored noise in theory. Assuming the equivalence of the two descriptions of the active bath, the active noise in the simulation system, which is directly extracted by fitting theoretical predictions to simulation measurements, is shown to depend on the constraint suffered by the passive tracer. This scenario is in significant contrast to the case of thermal noise that is independent of external trap potentials. The constraint dependence of active noise arises from the fact that the persistent force on the passive particle from the active bath can be influenced by the particle relaxation dynamics. Moreover, due to the interplay between the active collisions and particle relaxation dynamics, the effective temperature of the passive tracer quantified as the ratio of fluctuation to dissipation increases as the constraint strengthens, while the average potential and kinetic energies of the passive particle both decrease.

Drug resistance in breast cancer is based on the mechanism of exocrine non-coding RNA.

Breast cancer (BC) ranks first among female malignant tumors and involves hormonal changes and genetic as well as environmental risk factors. In recent years, with the improvement of medical treatment, a variety of therapeutic approaches for breast cancer have emerged and have strengthened to accommodate molecular diversity. However, the primary way to improve the effective treatment of breast cancer patients is to overcome treatment resistance. Recent studies have provided insights into the mechanisms of resistance to exosome effects in BC. Exosomes are membrane-bound vesicles secreted by both healthy and malignant cells that facilitate intercellular communication. Specifically, exosomes released by tumor cells transport their contents to recipient cells, altering their properties and promoting oncogenic components, ultimately resulting in drug resistance. As important coordinators, non-coding RNAs (ncRNAs) are involved in this process and are aberrantly expressed in various human cancers. Exosome-derived ncRNAs, including microRNAs (miRNAs), long-noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as crucial components in understanding drug resistance in breast cancer. This review provides insights into the mechanism of exosome-derived ncRNAs in breast cancer drug resistance, thereby suggesting new strategies for the treatment of BC.

RotNet: A Rotationally Invariant Graph Neural Network for Quantum Mechanical Calculations.

Deep learning has proven promising in biological and chemical applications, aiding in accurate predictions of properties such as atomic forces, energies, and material band gaps. Traditional methods with rotational invariance, one of the most crucial physical laws for predictions made by machine learning, have relied on Fourier transforms or specialized convolution filters, leading to complex model design and reduced accuracy and efficiency. However, models without rotational invariance exhibit poor generalization ability across datasets. Addressing this contradiction, this work proposes a rotationally invariant graph neural network, named RotNet, for accurate and accelerated quantum mechanical calculations that can overcome the generalization deficiency caused by rotations of molecules. RotNet ensures rotational invariance through an effective transformation and learns distance and angular information from atomic coordinates. Benchmark experiments on three datasets (protein fragments, electronic materials, and QM9) demonstrate that the proposed RotNet framework outperforms popular baselines and generalizes well to spatial data with varying rotations. The high accuracy, efficiency, and fast convergence of RotNet suggest that it has tremendous potential to significantly facilitate studies of protein dynamics simulation and materials engineering while maintaining physical plausibility.

Arsenic field test kits based on solid-phase fluorescence filter effect induced by silver nanoparticle formation.

Millions of people worldwide are affected by naturally occurring arsenic in groundwater. The development of a low-cost, highly sensitive, portable assay for rapid field detection of arsenic in water is important to identify areas for safe wells and to help prioritize testing. Herein, a novel paper-based fluorescence assay was developed for the on-site analysis of arsenic, which was constructed by the solid-phase fluorescence filter effect (SPFFE) of AsH3-induced the generation of silver nanoparticles (AgNPs) toward carbon dots. The proposed SPFFE-based assay achieves a low arsenic detection limit of 0.36 µg/L due to the efficient reduction of Ag+ by AsH3 and the high molar extinction coefficient of AgNPs. In conjunction with a smartphone and an integrated sample processing and sensing platform, field-sensitive detection of arsenic could be achieved. The accuracy of the portable assay was validated by successfully analyzing surface and groundwater samples, with no significant difference from the results obtained through mass spectrometry. Compared to other methods for arsenic analysis, this developed system offers excellent sensitivity, portability, and low cost. It holds promising potential for on-site analysis of arsenic in groundwater to identify safe well locations and quickly obtain output from the global map of groundwater arsenic.

An enhanced machine learning approach for effective prediction of IgA nephropathy patients with severe proteinuria based on clinical data.

IgA Nephropathy (IgAN) is a disease of the glomeruli that may eventually lead to chronic kidney disease or kidney failure. The signs and symptoms of IgAN nephropathy are usually not specific enough and are similar to those of other glomerular or inflammatory diseases. This makes a correct diagnosis more difficult. This study collected data from a sample of adult patients diagnosed with primary IgAN at the First Affiliated Hospital of Wenzhou Medical University, with proteinuria ≥1 g/d at the time of diagnosis. Based on these samples, we propose a machine learning framework based on weIghted meaN oF vectOrs (INFO). An enhanced COINFO algorithm is proposed by merging INFO, Cauchy Mutation (CM) and Oppositional-based Learning (OBL) strategies. At the same time, COINFO and Support Vector Machine (SVM) were integrated to construct the BCOINFO-SVM framework for IgAN diagnosis and prediction. Initially, the proposed enhanced COINFO is evaluated using the IEEE CEC2017 benchmark problems, with the outcomes demonstrating its efficient optimization capability and accuracy in convergence. Furthermore, the feature selection capability of the proposed method is verified on the public medical datasets. Finally, the auxiliary diagnostic experiment was carried out through IgAN real sample data. The results demonstrate that the proposed BCOINFO-SVM can screen out essential features such as High-Density Lipoprotein (HDL), Uric Acid (UA), Cardiovascular Disease (CVD), Hypertension and Diabetes. Simultaneously, the BCOINFO-SVM model achieves an accuracy of 98.56%, with sensitivity at 96.08% and specificity at 97.73%, making it a potential auxiliary diagnostic model for IgAN.

Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices.

Fin field-effect transistors (FinFETs) have been widely used in electronic devices on account of their excellent performance, but this new type of device is facing many challenges because of size constraints. Two-dimensional (2D) materials with a layer structure can meet the required thickness of FinFETs and provide ideal carrier transport performance. In this work, we used 2D tellurene as the parent material and modified it with doping techniques to improve electronic device performance. High-performance FinFET devices were prepared with 23 systems screened from 385 doping systems by a combination of first-principle calculations and a machine-learning (ML) model. Moreover, theoretical calculations demonstrated that 1S1@Te and 2S2@Te have high carrier mobility and stability with an electron mobility and a hole mobility of 6.211 × 104 cm2 V-1 S-1 and 1.349 × 104 cm2 V-1 S-1, respectively. This work can provide a reference for subsequent experiments and advance the development of functional materials by using an ML-assisted design paradigm.

A Data-Driven Platform for Two-Dimensional Hybrid Lead-Halide Perovskites.

The exceptional properties of two-dimensional hybrid organic-inorganic lead-halide perovskites (2D HOIPs) have led to a rapid increase in the number of low-dimensional materials for optoelectronic engineering and solar energy conversion. The flexibility and controllability of 2D HOIPs create a vast structural space, which presents an urgent issue to effectively explore 2D HOIPs with better performance for practical applications. However, the traditional RP-DJ classification method falls short in describing the influence of structure on the electronic properties of 2D HOIPs. To overcome this limitation, we employed inorganic structure factors (SF) as a classification descriptor, which considers the influence of inorganic layer distortion of 2D HOIPs. And we investigated the relationship between SF, other physicochemical features, and band gaps of 2D HOIPs. By using this structural descriptor as a feature for a machine learning model, a database of 304920 2D HOIPs and their structural and electronic properties was generated. A large number of previously neglected 2D HOIPs were discovered. With the establishment of this database, experimental data and machine learning methods were combined to develop a 2D HOIPs exploration platform. This platform integrates searching, download, analysis, and online prediction, providing a useful tool for the further discovery of 2D HOIPs.

Paper-assisted ratiometric fluorescent sensors for on-site sensing of sulfide based on the target-induced inner filter effect.

Dissolved sulfide tends to species transformation and loss upon leaving the matrix, thus the development of a practical on-site determination of sulfide is crucial for environmental monitoring and human health. In this work, a novel paper-based ratiometric fluorescence sensor was developed for the field analysis of sulfide, which system was constructed by the inner filter effect (IFE) of CdS quantum dots (QDs) toward carbon dots (C-dots). Instead of an aqueous phase system, the conversion of sulfide to its hydride would induce the in-situ formation of CdS QDs on the paper, which acted as an energy acceptor to quench the emission of C-dots, leading to a variation of ratiometric fluorescence from blue to yellow with the increasing concentration of sulfide. Moreover, we proposed a smartphone-based fluorescence capture device integrated with a programmed Python program, accomplishing both color recognition and accurate detection of sulfide. Under the optimal condition, this ratiometric fluorescence sensor allowed for the on-site analysis of sulfide with a limit of detection of 0.05 µM. The accuracy of the sensor was validated via the successful field analysis of environmental water samples with satisfactory recoveries. Compared to other fluorescence methods used for sulfide analysis, this developed system retains the advantages of label-free, low-cost, ease of operation, and miniaturization, showing great potential for the measurement of sulfide on-site, as well as environmental monitoring.