A new palladium complex as a dual fluorometric and colorimetric probe for rapid determination of sulfide anion

Activity-Based Fluorescent Probes for Hydrogen Sulfide and Related Reactive Sulfur Species

Hydrogen sulfide (H2S) is not only a well-established toxic gas but also an important small molecule bioregulator in all kingdoms of life. In contemporary biology, H2S is often classified as a "gasotransmitter," meaning that it is an endogenously produced membrane permeable gas that carries out essential cellular processes. Fluorescent probes for H2S and related reactive sulfur species (RSS) detection provide an important cornerstone for investigating the multifaceted roles of these important small molecules in complex biological systems. A now common approach to develop such tools is to develop "activity-based probes" that couple a specific H2S-mediated chemical reaction to a fluorescent output. This Review covers the different types of such probes and also highlights the chemical mechanisms by which each probe type is activated by specific RSS. Common examples include reduction of oxidized nitrogen motifs, disulfide exchange, electrophilic reactions, metal precipitation, and metal coordination. In addition, we also outline complementary activity-based probes for imaging reductant-labile and sulfane sulfur species, including persulfides and polysulfides. For probes highlighted in this Review, we focus on small molecule systems with demonstrated compatibility in cellular systems or related applications. Building from breadth of reported activity-based strategies and application, we also highlight key unmet challenges and future opportunities for advancing activity-based probes for H2S and related RSS.

Core-shell structured lignin-stabilized silver nanoprisms for colorimetric detection of sulfur ions

Widespread occurrence of sulfides in domestic and industrial wastewater contributes to environmental pollution and poses risks to human health. Therefore, the development of highly selective, sensitive, and rapid sulfur ion (S2-) detection probes in aquatic ecosystems is of paramount importance. In this study, lignin-stabilized silver nanoprisms (EHL@AgNPRs) were prepared using the seed growth and self-assembly methods. Based on this, a novel, high-performance, and environmentally friendly S2- colorimetric detection method was proposed. Lignin is believed to coat the surface of AgNPRs through cation-π and electrostatic interactions, acting as an excellent dispersant and stabilizer to prevent aggregation and shape deformation. This allows AgNPRs to maintain localized surface plasmon resonance (LSPR) characteristics and superior colorimetric sensing sensitivity towards S2- even after 30 d. The EHL@AgNPRs exhibited remarkable selectivity towards S2- with a minimum detection limit of 41.3 nM. The conjugation of lignin with AgNPRs offers a highly promising approach for the rapid detection of S2- in natural aquatic environments and for the valorization of lignin.

Intelligent quantitative recognition of sulfide using machine learning-based ratiometric fluorescence probe of metal-organic framework UiO-66-NH2/Ppix

Sulfides possess either high toxicity or play crucial physiological role such as gas transmitter dependent upon dosage, hence the significant for their rapid sensitive and selective concentration determination. Herein, a machine learning enhanced ratiometric fluorescence sensor was engineered for sulfide determination by incorporating the nanometal-organic framework (UiO-66-NH2) along with protoporphyrin IX (Ppix). The blue fluorescence at 431 nm originated from the moiety of UiO-66-NH2 by 365 nm excitation serves as an internal calibration reference signal, while the red fluorescence at 629 nm from the moiety of Ppix serves as the analytical signal, and the intensity is correlated to the amount of sulfides. The fluorescence color of the sensor gradually varies from blue to red upon sequential addition of copper and sulfide ions, resulting in RGB (Red, Green, Blue) feature values for corresponding sulfide concentrations, which facilities the advanced data processing techniques using machine learning algorithms. On the basis of fluorescence image fingerprint extraction and machine learning algorithms, an online data analysis model was developed to improve the precision and accuracy of sulfide determination. The established model employed Linear Discriminant Analysis (LDA) and was subjected to rigorous cross-validation to ensure its robustness. By analyzing the correlation between RGB feature values and sulfide concentrations, the study highlighted a significant positive relationship between the red feature values and sulfide concentrations. The application of machine learning techniques on the ratiometric fluorescence signal of the UiO-66-NH2/Ppix probe demonstrated its potential for intelligent quantitative determination of sulfides, offering a valuable and efficient tool for pollution detection and real-time rapid environmental monitoring.