Hasil untuk "Analytical chemistry"

R. J. Ramu, K. V. Anand, Jithin Jacob Varghese et al.

Purpose: To evaluate the effectiveness of Ventilator-Assisted Preoxygenation (VAPOX) compared with Bag Valve Mask (BVM) ventilation during Rapid Sequence Intubation (RSI) in patients presenting with acute respiratory distress. Materials and Methods: This observational study enrolled 60 critically ill patients requiring emergency intubation. Participants aged 40–90 years were consecutively allocated to receive preoxygenation by either VAPOX (n = 30) or BVM (n = 30). Heart rate, blood pressure, oxygen saturation (SpO2), and PaO2/FiO2 ratios were recorded at baseline, postpreoxygenation, and at 1, 5, and 30 minutes postintubation. Statistical analysis used SPSS software; significance was defined as P < 0.05. Results: Patients in the VAPOX group reached 95% SpO2 significantly faster than the BVM group (39 seconds vs. 80 seconds, P = 0.005), despite having a lower initial mean SpO2 (85.4% vs. 88.47%). Postpreoxygenation saturation was significantly higher with VAPOX (99.37%) than with BVM (96.90%) (P = 0.001). PaO2/FiO2 ratios at 5 minutes postintubation showed significant improvement in VAPOX compared with BVM (298.53 vs. 237.67; P = 0.007). Hemodynamic parameters showed no significant differences between groups. Conclusion: VAPOX is superior to BVM for preoxygenation during RSI in critically ill patients, demonstrating faster oxygenation, better maintenance of oxygen saturation, and significantly improved PaO2/FiO2 ratios, thereby minimizing transient and critical hypoxemia.

Reeta S. Tekam, Basant K. Ningawal, Pooja Vaskle et al.

Background: Managing pain after lower abdominal surgery is important for smooth recovery. Regional anesthesia techniques, like the transversus abdominis plane (TAP) block and ilioinguinal–iliohypogastric (II/IH) nerve block, help reduce opioid use. This study compares the effectiveness of these two blocks in pain control, time to first rescue dose, hemodynamic stability, and side effects. Materials and Methods: A comparative study was done on 100 patients having lower abdominal surgeries under spinal anesthesia. Patients were divided into two groups: Group A (n = 50): Received bilateral TAP block with 20 ml of 0.375% ropivacaine per side. Group B (n = 50): Received bilateral II/IH block with the same drug and volume. Pain scores (VAS) were noted at different time points up to 48 hours. Time to first rescue analgesia, vitals, and side effects were recorded. Results: VAS scores at 4 to 24 hours were lower in the TAP group. Time to first rescue dose was longer in the TAP Group A (9.35 ± 0.47 hours) vs. the II/IH Group B (6.97 ± 1.16 hours). Hemodynamics and side effects were similar. Conclusion: TAP block offers better pain relief and longer duration than II/IH block in abdominal surgeries.

Mukesh Soni, Shivani Mishra, Madhu S. Ratre et al.

Background: The accumulation of microbial biofilm around dental implants remains a significant concern in maintaining peri-implant health. Ineffective cleaning of the peri-implant mucosa can result in peri-implantitis, jeopardizing implant longevity. This study evaluates the influence of different peri-implant mucosa cleaning protocols on microbial biofilm formation. Materials and Methods: A total of 60 patients with dental implants were included in this randomized controlled trial. Participants were divided into three groups (n = 20 per group) based on the cleaning protocol: Group 1 (manual brushing with soft bristles), Group 2 (ultrasonic cleaning), and Group 3 (air polishing). Biofilm accumulation was assessed at baseline, 1 month, and 3 months using the Plaque Index (PI) and microbial colony-forming units (CFU). Statistical analysis was performed using one-way ANOVA to determine significant differences between groups. Results: At the end of the study, Group 3 (air polishing) demonstrated the least biofilm accumulation, with a mean PI score reduction of 70% compared to baseline. Group 2 (ultrasonic cleaning) showed a 50% reduction, whereas Group 1 (manual brushing) exhibited a 30% reduction. Microbial analysis revealed the lowest CFU counts in Group 3 (1500 ± 200 CFU), followed by Group 2 (3200 ± 300 CFU) and Group 1 (5000 ± 400 CFU). Statistically significant differences (P < 0.05) were observed between the groups at 1 and 3 months. Conclusion: Air polishing proved to be the most effective peri-implant mucosa cleaning protocol for reducing microbial biofilm formation, followed by ultrasonic cleaning. Manual brushing alone showed limited efficacy in controlling biofilm. Implementing efficient cleaning protocols is crucial for preventing peri-implant diseases and enhancing implant success.

Shiva Moraveji, Lida Fotouhi, Saeed Shahrokhian et al.

Abstract Transition metal chalcogenides are promising compounds in electrochemistry with different applications including energy conversion/storage devices due to their stability, different oxidation states, and highly active surface areas. In this work, ZnCoTe nanorods coated with NiCoSe2 as the shell have been synthesized by a two-step method. ZnCoTe@NiCoSe2 has an excellent specific capacity of 240 mAh g−1 at a current density of 2 A g−1 in a three-electrode system containing 3 M KOH electrolyte solution, which is much superior to each of the core and shell components of the constructed electrodes. Furthermore, a remarkable cycle life of 94% is obtained at a current density of 10 A g−1 after 5000 cycles, suggesting its long-term stability. The hybrid supercapacitor consists of ZnCoTe@NiCoSe2 and activated carbon as the positive and negative electrodes, respectively. In addition, ZnCoTe@NiCoSe2//AC (HSC device) showed 580 W kg−1 power density at an energy density of 57 Wh kg−1. Additionally, the device retained 90% of its initial capacitance after 5000 cycles. Eventually, considering the excellent electrical conductivities and large numbers of active sites, transition metal chalcogenides have been utilized as efficient electrodes for supercapacitor applications. This work introduces a method for the incorporation of zinc into the telluride matrix, which modulates the electronic structure, enhances intrinsic electrical conductivity, and increases the density of electroactive sites. Moreover, the engineered core–shell architecture of ZnCoTe@NiCoSe₂ provides a synergistic combination of high electrical conductivity, abundant active sites, and a well-organized hierarchical structure. These integrated features significantly boost charge storage performance and overall efficiency of the supercapacitor device.

Eric C. -Y. Yuan, Yunsheng Liu, Junmin Chen et al.

Given the power of large language and large vision models, it is of profound and fundamental interest to ask if a foundational model based on data and parameter scaling laws and pre-training strategies is possible for learned simulations of chemistry and materials. The scaling of large and diverse datasets and highly expressive architectures for chemical and materials sciences should result in a foundation model that is more efficient and broadly transferable, robust to out-of-distribution challenges, and easily fine-tuned to a variety of downstream observables, when compared to specific training from scratch on targeted applications in atomistic simulation. In this Perspective we aim to cover the rapidly advancing field of machine learned interatomic potentials (MLIP), and to illustrate a path to create chemistry and materials MLIP foundation models at larger scale.

Jaron Cohen, Alexander G. Hasson, Sara Tanovic

Since the advent of machine learning, interpretability has remained a persistent challenge, becoming increasingly urgent as generative models support high-stakes applications in drug and material discovery. Recent advances in large language model (LLM) architectures have yielded chemistry language models (CLMs) with impressive capabilities in molecular property prediction and molecular generation. However, how these models internally represent chemical knowledge remains poorly understood. In this work, we extend sparse autoencoder techniques to uncover and examine interpretable features within CLMs. Applying our methodology to the Foundation Models for Materials (FM4M) SMI-TED chemistry foundation model, we extract semantically meaningful latent features and analyse their activation patterns across diverse molecular datasets. Our findings reveal that these models encode a rich landscape of chemical concepts. We identify correlations between specific latent features and distinct domains of chemical knowledge, including structural motifs, physicochemical properties, and pharmacological drug classes. Our approach provides a generalisable framework for uncovering latent knowledge in chemistry-focused AI systems. This work has implications for both foundational understanding and practical deployment; with the potential to accelerate computational chemistry research.

Niklas Geue

Dušan Veličković, Kevin J. Zemaitis, Arunima Bhattacharjee et al.

ABSTRACTAliphatic carboxylic acids, aldehydes, and ketones play diverse roles in microbial adaptation to their microenvironment, from excretion as toxins to adaptive metabolites for membrane fluidity. However, the spatial distribution of these molecules throughout biofilms and how microbes in these environments exchange these molecules remain elusive for many of these bioactive species due to inefficient molecular imaging strategies. Herein, we apply on-tissue chemical derivatization (OTCD) using 4-(2-((4-bromophenethyl)dimethylammonio)ethoxy)benzenaminium dibromide (4-APEBA) on a co-culture of a soil bacterium (Bacillus subtilis NCIB 3610) and fungus (Fusarium sp. DS 682) grown on agar as our model system. Using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), we spatially resolved more than 300 different metabolites containing carbonyl groups within this model system. Various spatial patterns are observable in these species, which indicate possible extracellular or intercellular processes of the metabolites and their up- or downregulation during microbial interaction. The unique chemistry of our approach allowed us to bring additional confidence in accurate carbonyl identification, especially when multiple isomeric candidates were possible, and this provided the ability to generate hypotheses about the potential role of some aliphatic carbonyls in this B. subtilis/Fusarium sp. interaction. The results shown here demonstrate the utility of 4-ABEBA-based OTCD MALDI-MSI in probing interkingdom interactions directly from microbial co-cultures, and these methods will enable future microbial interaction studies with expanded metabolic coverage.IMPORTANCEThe metabolic profiles within microbial biofilms and interkingdom interactions are extremely complex and serve a variety of functions, which include promoting colonization, growth, and survival within competitive and symbiotic environments. However, measuring and differentiating many of these molecules, especially in an in situ fashion, remains a significant analytical challenge. We demonstrate a chemical derivatization strategy that enabled highly sensitive, multiplexed mass spectrometry imaging of over 300 metabolites from a model microbial co-culture. Notably, this approach afforded us to visualize over two dozen classes of ketone-, aldehyde-, and carboxyl-containing molecules, which were previously undetectable from colonies grown on agar. We also demonstrate that this chemical derivatization strategy can enable the discrimination of isobaric and isomeric metabolites without the need for orthogonal separation (e.g., online chromatography or ion mobility). We anticipate that this approach will further enhance our knowledge of metabolic regulation within microbiomes and microbial systems used in bioengineering applications.

Daria A. Avdanina, Alexander A. Zhgun

Abstract The vast majority of cultural heritage objects consist of materials that can be subjected to biodeterioration. Currently, there is significant number of publications showing which materials are capable of destroying certain organisms, and what conservation and restoration procedures are required. However, there is neither a clear classification of these diverse events nor their visual representation. In our review, for the first time, an attempt is made to compare the type of biodeterioration, based on the destruction of a particular material, with a specific color of the rainbow. In this regard, a cultural heritage objects made of a single material are designated as one color icon; and those made of composite materials are designated as pictogram consisting of several icons of corresponding colors. For example, a stone sculpture, in accordance with the rainbow code, is assigned a gray color, which was introduced to visualize stone materials. The drum corresponds to a pictogram consisting of violet (corresponds to leather) and brown (corresponds to wood). A work of easel painting on canvas corresponds to a pictogram consisting of a red color icon (corresponds to canvas) and a gold color icon (corresponds to painting materials). We used cold color shades to denote basic inorganic materials, and cold color shades to denote organic materials. The proposed rainbow code for biodetermination is an open platform that can be expanded by adding new colors for new materials introduced, and allows to translate potentially any cultural heritage object into a pictogram with colors that correspond to the materials used in its manufacture. Such a graphical interpretation can help both systematize the storage conditions of museum exhibits and facilitate understanding of the processes of biodeterioration of composite materials. Graphical Abstract

Ruchita Rajput, Sonal Desai

Assessing the greenness of sample preparation techniques is essential due to the use of various solvents, chemicals, reagents, sorbents, pH adjustments, and energetic inputs. Solid-Phase Microextraction (SPME) is preferred over traditional flavor extraction methods such as Simultaneous Distillation Extraction (SDE) and Solvent-Assisted Flavor Evaporation (SAFE) because of its sensitivity, efficiency, speed, versatility, and economy. SPME fibers coated with stationary phases can extract volatile and semi-volatile flavor compounds from food samples, which are then desorbed and analyzed by Gas Chromatography-Mass Spectrometry (GC–MS). This article provides an assessment of the greenness of 50 SPME techniques for flavor analysis using the AGREE Prep and Sample Preparation Metric of Sustainability (SPMS) tools. This evaluation offers valuable insights into the environmental impact and sustainability of SPME as sample preparation techniques for flavour analysis. As per AGREE Prep, method 34 was found to be environmental friendly with score of 0.66, attributed to safe solvents, minimized waste, high sample throughput, and low energy consumption where as method 7 with SPMS scores of 7.05 is more sustainable due to miniaturization, fewer steps, and low energy use.

J. Jayanthi, P. Uma Maheswari

Abstract In recent times, there has been a proactive effort by various institutions and organizations to preserve historic manuscripts as repositories of traditional knowledge and cultural heritage. Leveraging digital media and emerging technologies has proven to be an efficient way to safeguard these invaluable documents. Such technologies not only facilitate the extraction of knowledge from historic manuscripts but also hold promise for global applications. However, transforming inscribed stone artifacts into binary formats presents significant challenges due to angle distortion, subtle differences between foreground and background, background noise, variations in text size, and related issues. A pivotal aspect of effective image processing in preserving the rich information and wisdom encoded in stone inscriptions lies in employing appropriate pre-processing methods and techniques. This research paper places a special focus on elucidating various preprocessing techniques, encompassing resizing, grayscale conversion, enhancement of brightness and contrast, smoothening, noise removal, morphological operations, and thresholding. To comprehensively assess these techniques, we undertake a study involving stone inscription images extracted from the Tanjore Brihadeeswar Temple, dating back to the eleventh century during the reign of Raja Raja Chola. This choice is informed by the manifold challenges associated with image correction, such as distortion and blurring. We undertake an evaluation encompassing a diverse array of stone background structures, including types like flawless-bright-moderately legible, dark-illegible, flawless-bright-illegible, flawless-dull, flawless-irregular-moderate, highly impaired-dark-legible, highly impaired-irregular-illegible, impaired-dark-moderate, impaired-dull-moderately legible, impaired-dusky dark-moderate, and very impaired-dusky dark-legible. Subsequently, the processed outputs are subjected to character recognition and information extraction, with a focus on comparing the outcomes of various pre-processing methods, including binarization and grayscale conversion. This study seeks to contribute insights into the most effective pre-processing strategies for enhancing the legibility and preservation of ancient Indian script images etched onto diverse stone background structures.

Inna V. Fadeeva, Katia Barbaro, Annalisa Altigeri et al.

Addressing periprosthetic infections, which present significant healing challenges that often require revision surgeries, necessitates the development of novel antibacterial materials and implants. Current research focuses on creating materials that hinder bacterial adhesion, colonization, and proliferation in surrounding tissues. Boron (B)-containing compounds are known for their antibacterial properties and potential in bone metabolism for regenerative medicine. In this study, we synthesized B-containing tricalcium phosphate (0.3B-TCP) with 1.1 wt.% B content via precipitation from aqueous solutions and sintering at 1100 °C. X-ray diffraction confirmed the ceramic’s primary crystalline phase as β-TCP, with B evenly distributed according to energy-dispersive spectroscopy data. Electron paramagnetic resonance (EPR) data verified stable paramagnetic borate anions, indicating successful BO₃³⁻ substitution for phosphate groups. The microstructural properties of 0.3B-TCP ceramic were assessed before and after soaking in a saline solution. Its bending strength was approximately 30 MPa, and its porosity was about 33%. 0.3B-TCP ceramic demonstrated significant antimicrobial efficacy against various bacterial strains and a fungus. Cytotoxicity evaluation using equine adipose tissue-derived mesenchymal stem cells and osteogenic differentiation assessment were conducted. The combination of antibacterial efficacy and good cytocompatibility suggests 0.3B-TCP ceramic as a promising bone substitute material.

Mayk Caldas Ramos, Christopher J. Collison, Andrew D. White

Large language models (LLMs) have emerged as powerful tools in chemistry, significantly impacting molecule design, property prediction, and synthesis optimization. This review highlights LLM capabilities in these domains and their potential to accelerate scientific discovery through automation. We also review LLM-based autonomous agents: LLMs with a broader set of tools to interact with their surrounding environment. These agents perform diverse tasks such as paper scraping, interfacing with automated laboratories, and synthesis planning. As agents are an emerging topic, we extend the scope of our review of agents beyond chemistry and discuss across any scientific domains. This review covers the recent history, current capabilities, and design of LLMs and autonomous agents, addressing specific challenges, opportunities, and future directions in chemistry. Key challenges include data quality and integration, model interpretability, and the need for standard benchmarks, while future directions point towards more sophisticated multi-modal agents and enhanced collaboration between agents and experimental methods. Due to the quick pace of this field, a repository has been built to keep track of the latest studies: https://github.com/ur-whitelab/LLMs-in-science.

Meghana Velegar, Christoph Keller, J. Nathan Kutz

We introduce the optimized dynamic mode decomposition algorithm for constructing an adaptive and computationally efficient reduced order model and forecasting tool for global atmospheric chemistry dynamics. By exploiting a low-dimensional set of global spatio-temporal modes, interpretable characterizations of the underlying spatial and temporal scales can be computed. Forecasting is also achieved with a linear model that uses a linear superposition of the dominant spatio-temporal features. The DMD method is demonstrated on three months of global chemistry dynamics data, showing its significant performance in computational speed and interpretability. We show that the presented decomposition method successfully extracts known major features of atmospheric chemistry, such as summertime surface pollution and biomass burning activities. Moreover, the DMD algorithm allows for rapid reconstruction of the underlying linear model, which can then easily accommodate non-stationary data and changes in the dynamics.

Andrew D. McNaughton, Gautham Ramalaxmi, Agustin Kruel et al.

Large language models (LLMs) have shown remarkable potential in various domains, but they often lack the ability to access and reason over domain-specific knowledge and tools. In this paper, we introduced CACTUS (Chemistry Agent Connecting Tool-Usage to Science), an LLM-based agent that integrates cheminformatics tools to enable advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama2-7b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b and Mistral-7b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with domain-specific tools, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment. Furthermore, CACTUS represents a significant milestone in the field of cheminformatics, offering an adaptable tool for researchers engaged in chemistry and molecular discovery. By integrating the strengths of open-source LLMs with domain-specific tools, CACTUS has the potential to accelerate scientific advancement and unlock new frontiers in the exploration of novel, effective, and safe therapeutic candidates, catalysts, and materials. Moreover, CACTUS's ability to integrate with automated experimentation platforms and make data-driven decisions in real time opens up new possibilities for autonomous discovery.

Debarshi Banerjee, Khatereh Azizi, Colin K. Egan et al.

The use of computer simulations to study the properties of aqueous systems is, today more than ever, an active area of research. In this context, during the last decade there has been a tremendous growth in the use of data-driven approaches to develop more accurate potentials for water as well as to characterize its complexity in chemical and biological contexts. We highlight the progress, giving a historical context, on the path to the development of many-body and reactive potentials to model aqueous chemistry, including the role of machine learning strategies. We focus specifically on conceptual and methodological challenges along the way in performing simulations that seek to tackle problems in modeling the chemistry of aqueous solutions. In conclusion, we summarize our perspectives on the use and integration of advanced data-science techniques to provide chemical insights in physical chemistry and how this will influence computer simulations of aqueous systems in the future.

Harrison Nicholls, Eric Hébrard, Olivia Venot et al.

The effect of enhanced UV irradiation associated with stellar flares on the atmospheric composition and temperature of gas giant exoplanets was investigated. This was done using a 1D radiative-convective-chemical model with self-consistent feedback between the temperature and the non-equilibrium chemistry. It was found that flare-driven changes to chemical composition and temperature give rise to prolonged trends in evolution across a broad range of pressure levels and species. Allowing feedback between chemistry and temperature plays an important role in establishing the quiescent structure of these atmospheres, and determines their evolution due to flares. It was found that cooler planets are more susceptible to flares than warmer ones, seeing larger changes in composition and temperature, and that temperature-chemistry feedback modifies their evolution. Long-term exposure to flares changes the transmission spectra of gas giant atmospheres; these changes differed when the temperature structure was allowed to evolve self-consistently with the chemistry. Changes in spectral features due to the effects of flares on these atmospheres can be associated with changes in composition. The effects of flares on the atmospheres of sufficiently cool planets will impact observations made with JWST. It is necessary to use self-consistent models of temperature and chemistry in order to accurately capture the effects of flares on features in the transmission spectra of cooler gas giants, but this depends heavily on the radiation environment of the planet.

Emilie Panek, Jean-Philippe Beaulieu, Pierre Drossart et al.

Studying chemistry and chemical composition is fundamental to go back to formation history of planetary systems. We propose here to have another look at five targets to better determine their composition and the chemical mechanisms that take place in their atmospheres. We present a re-analysis of five Hot Jupiters, combining multiple instruments and using Bayesian retrieval methods. We compare different combinations of molecules present in the simulated atmosphere, different chemistry types as well as different clouds parametrization. As a consequence of recent studies questioning the detection of Na and K in the atmosphere of HD 209458b as being potentially contaminated by stellar lines when present, we study the impact on other retrieval parameters of misinterpreting the presence of these alkali species. We use spatially scanned observations from the grisms G102 and G141 of the WFC3 on HST, with a wavelength coverage of $\sim$0.8 to $\sim$1.7 microns. We analyse these data with the publicly available Iraclis pipeline. We added to our datasets STIS observations to increase our wavelength coverage from $\sim$0.4 to $\sim$1.7 microns. We then performed a Bayesian retrieval analysis with the open-source TauREx using a nested sampling algorithm. We explore the influence of including Na and K on the retrieval of the molecules from the atmosphere. Our data re-analysis and Bayesian retrieval are consistent with previous studies but we find small differences in the retrieved parameters. After all, Na and K has no significant impact on the properties of the planet atmospheres. Therefore, we present here our new best-fit models, taking into account molecular abundances varying freely and equilibrium chemistry. This work is a preparation for a future addition of more sophisticated representation of chemistry taking into account disequilibrium effects such as vertical mixing and photochemistry.

Fernanda S. Luccas, PhD in Graduate Program in Sciences, Elisabete A. De Nadai Fernandes, Yuniel T. Mazola et al.

Mass spectrometry-based techniques have been used to study the chemical profile of honeys to authenticate entomological, botanical and geographical origins. Sample preparation is a crucial step of the analysis to obtaining reliable data and minimizing interference owing to matrix effects. The present work studied the best sample digestion procedure for elemental analysis of Brazilian honeys from Tetragonisca angustula (Jataí) and Apis mellifera sp (Apis) by triple quadrupole inductively coupled plasma mass spectrometry (TQ-ICP-MS). A central composite design with 2² factorial and 3 center points considering different volumes of HNO3 and H2O2 was investigated. There was no statistically significant influence of the amounts of HNO3 and H2O2 on the recoveries of Ag, Al, As, Ba, Be, Ca, Cd, Ce, Co, Cr, Cs, Cu, K, La, Mg, Mn, Na, Ni, Pb, Rb, Se, Sr, Th, U, V and Zn mass fractions. Machine learning algorithms (Multilayer Perceptron, Random Forest and Support Vector Machine) allowed discriminating entomological origin of honeys based on chemical profile with a classification accuracy of 99%.

Mohamed A. Abdel-Lateef, Albandary Almahri, Eman Alzahrani et al.

In the proposed method, microwave-assist heating and AgNO3/trisodium citrate were used to create the polyvinylpyrrolidone-capped silver nanoparticles (PVP-AgNPs) sensor. This sensor had a peroxidase-like activity that could catalytically oxidize <i>O</i>-phenylenediamine (OPD, colourless) into 2,3-diaminophenazine (ox-OPD, greenish-yellow colour) in the presence of H₂O₂, otherwise, in the presence of Hg²⁺, this pass has been effectively inhibited. The degree of colour fading was directly correlated with Hg²⁺ concentration. These results indicated the selectivity of Hg²⁺ ions toward PVP-AgNPs after establishing the PVP-AgNPs/OPD/H₂O₂ system. This selectivity was proved by the negative results obtained from other <i>mon</i>-, <i>di</i>-, and <i>tri</i>valent ions such as Na⁺, K⁺, Ca²⁺, Mg²⁺, Ba²⁺, Co²⁺, Ni²⁺, Cd²⁺, and Cr³⁺, instead of Hg²⁺. Consequently, a reliable, selective, and eco-effective spectrophotometric approach was designed for the detection of Hg²⁺ in various types of water samples. LOD was extended to lower than 0.1 µM, and a fading in the obtained colour was shown by the naked eye at a concentration higher than 1.5 µM of Hg²⁺. The elemental details for preparing the used PVP-AgNPs, such as particle size, morphology, polydispersity index (PdI), and their UV-visible spectrum, were identified by SEM technique, TEM, UV-visible spectrophotometer, and zeta-sizer device. Thus, the peroxidase mimicking the activity of OPD/H₂O₂ was confirmed by a fluorescence technique. The greenness profile of this work was confirmed after applying a reported assessment tool.