This review is focused on recent developments of surface-enhanced Raman scattering (SERS) applications in Analytical Chemistry. The work covers advances in the fabrication methods of SERS substrates, including nanoparticles immobilization techniques and advanced nanopatterning with metallic features. Recent insights in quantitative and sampling methods for SERS implementation and the development of new SERS-based approaches for both qualitative and quantitative analysis are discussed. The advent of methods for pre-concentration and new approaches for single-molecule SERS quantification, such as the digital SERS procedure, has provided additional improvements in the analytical figures-of-merit for analysis and assays based on SERS. The use of metal nanostructures as SERS detection elements integrated in devices, such as microfluidic systems and optical fibers, provided new tools for SERS applications that expand beyond the laboratory environment, bringing new opportunities for real-time field tests and process monitoring based on SERS. Finally, selected examples of SERS applications in analytical and bioanalytical chemistry are discussed. The breadth of this work reflects the vast diversity of subjects and approaches that are inherent to the SERS field. The state of the field indicates the potential for a variety of new SERS-based methods and technologies that can be routinely applied in analytical laboratories.
Colorimetric techniques have been developed and used in routine analyses for over a century and apparently all their potentialities have been exhaustively explored. However, colorimetric techniques have gained high visibility in the last two decades mainly because of the development of the miniaturization concept, for example, paper-based analytical devices that mostly employ colorimetric reactions, and by the advances and popularity of image capture instruments. The impressive increase in the use of these devices was followed by the development and enhancement of different modes of color detection to meet the demands of making qualitative, semi-quantitative, and fully quantitative analyses of multiple analytes. Cameras, scanners, and smartphones are now being used for this purpose and have become suitable alternatives for different approaches to colorimetric analysis; this, in addition to advancements in miniaturized devices. On the other hand, recent developments in optoelectronics technologies have launched more powerful, more stable and cheaper light-emitting diodes (LEDs), which once again have become an interesting tool for the design of portable and miniaturized devices based on colored reactions. Here, we present a critical review of recent developments and challenges of colorimetric detection in modern analytical chemistry in the last five years, and present thoughts and insights towards future perspectives in the area to improve the use of colorimetric detection in different application approaches.
Á. Santana-Mayor, R. Rodríguez-Ramos, A. Herrera‐Herrera
et al.
Abstract The incessant effort to perform a more sustainable chemistry has brought about the development of new materials that accomplish the principles of the green chemistry. In this context, deep eutectic solvents (DESs) have surged as one of the most promising alternatives to the use of toxic organic solvents. Their unique properties have led to a huge development of these materials and a sharp increase of their applications in the recent years in analytical chemistry. They have been applied in sample preparation and analytical techniques. The quantity of new information generated from studies associated with DESs is enormous and needs to be continuously revised. In this review, the most relevant aspects related to the application of DESs in the last five years (2016-2020) have been compiled and critically discussed in order to provide a global view about the advantages and limitations of these new materials in the area of analytical chemistry.
Relevance. In the industrially tense region (Donbass), as a result of socio-economic upheavals since 2014, many lands have been withdrawn from agricultural use and are now abandoned and degrading. Areas of active military action create beligerative landscapes characterized by profound geophysical and geochemical transformations. These areas are hotbeds of toxic environmental impacts and require targeted restoration measures. Phytoremediation stands out among the most effective methods for optimizing natural-territorial complexes of the DPR as the most effective, economically advantageous and aesthetically attractive.Materials and Methods. Agricultural and recreational ecotopes in the Central Donbass were studied. A field assessment of the state of local geosystems was conducted. Morphological analysis and description of plants, as well as calculations for determining life strategies (CSR), were applied. Analytical methods (atomic absorption, inductively coupled plasma mass spectrometry, and neutron activation) were used.Results. A difference in the range of informative structural features variation of some indicator plants for use in phytoremediation purposes in post-conflict areas – sites of active military operations in Donbass – has been established. New geochemical anomalies were identified in post-conflict areas for a number of technophile elements (Mn, Р, Zn, Cu, Mo, Ni, Pb, Cr, La, Co, Se, As, Cd). For the plant species Cichorium intybus L., Taraxacum officinale F.H.Wigg, Plantago major L., and Diplotaxis muralis (L.) DC., the implementation patterns of life-sustaining strategies (visualization of CSR in the Grime-Ramensky triangle) and ecological plasticity in areas affected by the militarization of the region were determined. Anatomical and morphological pathologies of the studied species were identified. The ecological valence of species allows them to support the initial stages of active succession during the first two to three years, forming a vegetation cover that performs anti-erosion and habitatforming functions. Based on plant morphopathologies and elemental composition data, geochemical anomalies were identified and a range of geochemical background values for elemental composition in plant samples was described. A phosphorus-lanthanum anomaly (P-La), a consequence of military operations in the DPR, is described for the first time.
Monika Gandhi, Rashpal Singh Gill, Aishwarya Sharma
et al.
Background:
Proper pain control before subarachnoid block (SAB) is must in proximal femur fractures as patients cannot tolerate movement due to severe pain. This study compares ultrasound-guided fascia iliaca compartment block (USG-FICB) with femoral nerve block (USG-FNB) for pre-SAB analgesia.
Materials and Methods:
Ninety patients scheduled for elective femur fracture surgery under SAB were randomly divided. Group FICB received 25 ml of 0.25% bupivacaine and Group FNB got 15 ml. Pain was assessed using Numerical Rating Scale (NRS) scale. Time for first rescue dose, side effects, and patient satisfaction also noted.
Results:
Both groups had similar baseline NRS. Group FICB showed faster pain relief and better positioning comfort during SAB (NRS 3.00 vs. 4.28 P = 0.001). Time to first rescue analgesia was more in FICB (4.67 vs. 2.71 h P = 0.001). Satisfaction scores were also higher. Hemodynamics stayed stable, and adverse events were rare.
Conclusion:
USG-FICB is more effective than USG-FNB in controlling pain before SAB in femur fracture surgery.
Kourosh Darvish, Arjun Sohal, Abhijoy Mandal
et al.
Accelerated materials discovery is critical for addressing global challenges. However, developing new laboratory workflows relies heavily on real-world experimental trials, and this can hinder scalability because of the need for numerous physical make-and-test iterations. Here we present MATTERIX, a multiscale, graphics processing unit-accelerated robotic simulation framework designed to create high-fidelity digital twins of chemistry laboratories, thus accelerating workflow development. This multiscale digital twin simulates robotic physical manipulation, powder and liquid dynamics, device functionalities, heat transfer and basic chemical reaction kinetics. This is enabled by integrating realistic physics simulation and photorealistic rendering with a modular graphics processing unit-accelerated semantics engine, which models logical states and continuous behaviors to simulate chemistry workflows across different levels of abstraction. MATTERIX streamlines the creation of digital twin environments through open-source asset libraries and interfaces, while enabling flexible workflow design via hierarchical plan definition and a modular skill library that incorporates learning-based methods. Our approach demonstrates sim-to-real transfer in robotic chemistry setups, reducing reliance on costly real-world experiments and enabling the testing of hypothetical automated workflows in silico. The project website is available at https://accelerationconsortium.github.io/Matterix/ .
J. Sempionatto, Itthipon Jeerapan, S. Krishnan
et al.
With the emergence of mobile devices and digital medicine, wearable sensors have received tremendous recent attention across many applications related to monitoring the wearer's conditions and surroundings. Existing wearable sensors commonly track the user's mobility and vital signs (steps, heart rate, etc.). The recent introduction of non-invasive chemical sensors, providing continuous monitoring of chemical markers in a non-invasive manner, fills major gaps in wearable sensor technology, as desired for a plethora of applications. This emerging and exciting area of on-body wearable chemical sensing represents a major transition away from common centralized laboratory-based analytical systems involving in-vitro test-tube assays of blood or urine. Such a major revolution has led to a variety of wearable chemical sensors that allow non-invasive continuous monitoring of many important analytes in biofluids, such as sweat, saliva, tears and interstitial fluid (ISF), instead of blood.
The last 10 years have witnessed the growth of artificial intelligence into different research areas, emerging as a vibrant discipline with the capacity to process large amounts of information and even intuitively interact with humans. In the chemical world, these innovations in both hardware and algorithms have allowed the development of revolutionary approaches in organic synthesis, drug discovery, and materials' design. Despite these advances, the use of AI to support analytical purposes has been mostly limited to data-intensive methodologies linked to image recognition, vibrational spectroscopy, and mass spectrometry but not to other technologies that, albeit simpler, offer promise of greatly enhanced analytics now that AI is becoming mature enough to take advantage of them. To address the imminent opportunity of analytical chemists to use AI, this tutorial review aims to serve as a first step for junior researchers considering integrating AI into their programs. Thus, basic concepts related to AI are first discussed followed by a critical assessment of representative reports integrating AI with various sensors, spectroscopies, and separation techniques. For those with the courage (and the time) needed to get started, the review also provides a general sequence of steps to begin integrating AI into their programs.
Oxidative stress is a detrimental condition that occurs when there is an imbalance between sub-cellular antioxidants and free radicals. Endogenous antioxidants actively scavenge free radicals and prevent oxidative stress. Pesticides can affect antioxidant activities. However, molecular interactions between the pesticides and endogenous antioxidants are not clear. Hence, objective of the present study is to dissect the intermolecular interactions between the widely-used pesticides (4,4′-dichlorodiphenyldichloroethylene, 4,4′-DDE; imidacloprid, IMD; lambda-cyhalothrin, CYH; malathion, MAL) and major antioxidant enzymes (Glutathione peroxidase-4, Glutathione S-Transferase, Catalase, Glutathione reductase, Superoxide dismutase-1) that might be responsible for altered enzyme activities and induction of oxidative stress. The binding affinity analysis using AutoDock vina and Discovery Studio Visualizer was conducted to unveil the potential intermolecular interactions between the pesticides and antioxidants. In the results, 4,4′-DDE, IMD, CYH, and MAL interacted with the antioxidants through stable hydrogen bonds, carbon-hydrogen bonds, van der Waals, and other hydrophobic interactions. Additionally, considerable binding affinities between the pesticides and enzymes were noted. ADME analyses have also revealed that the majority of pesticides can cross GI, exhibit low to high water solubility, and follow Lipinski's rule. Hence, results of the present study suggest that pesticides can potentially interact with antioxidants to modulate their catalytic activity and induce oxidative stress.
Shruti Vishwakarma, Gautam Kumar, Ritika Jain
et al.
Background:
Chronic alcohol consumption does not just undermine physical health it also quietly chips away at closeness and emotional connection. One of its most ignored results is sexual dysfunction. This study looked at how male sexual function is affected by alcohol consumption and how its severity affects the degree of impairment.
Methods:
A cross-sectional study of 162 alcohol-dependent men and 50 age-matched controls was conducted at the psychiatry OPD of a tertiary care hospital. Administered were validated instruments Alcohol Use Disorders Identification Test (AUDIT) for alcohol consumption severity, Premature Ejaculation Diagnostic Tool (PEDT) for ejaculation and International Index of Erectile Function (IIEF) for erectile and sexual domains.
Results:
Nearly 8 of 10 alcohol-dependent men (77.2%) reported some sort of sexual dysfunction, compared with 44.0% in controls (P < 0.001). Of those, erectile dysfunction (63.0% vs. 18.0%) was the most prevalent, followed by decreased sexual desire (57.4% vs. 28.0%) and early ejaculation (46.3% vs. 28.0%). Significantly, more AUDIT scores were related to more dysfunction in all sexual domains.
Conclusion:
More drinking, less intimacy the link is obvious. Acknowledging and handling sexual health in these people could help not just their relationships but also their road to recovery.
Bhavika A. Bhavsar, Vaishali Jain, Annu Kumari
et al.
Objective:
To Evaluate The Antimicrobial Properties of Double Antibiotic Paste and Morinda citrifolia And Propolis Paste Used In Regenerative Endodontics.
Materials and Method:
Using intact human teeth, radicular dentin samples were prepared (dimensions 4×4×2 mm) by following a standard method. The samples were cut using a diamond saw at minimal speed using water as coolant and smoothed with abrasive papers. To expose the dentin tubules and eliminate the smear layer, they were treated with NaOCl (1.5%), distilled water, and EDTA (17%). A total of 30 dentin samples went through a three-week anaerobic infection with bacterial biofilms sourced from the root canal of an underdeveloped tooth showing pulp necrosis. Group 1 acted as the control, while Group 2 received treatment with DAP, and Group 3 was treated with a paste of Morinda citrifolia and Propolis. After a week, pastes were cleaned, and specimens were dipped in phosphate-buffered saline for a day. The biofilm was released from each specimen by sonicating and vortexing it for 30 seconds. It was then diluted, spiral-plated on blood agar, and cultured anaerobically for a day. An automated colony counter was utilized to measure colony-forming units (CFU/mL), and statistical analysis was conducted using the Wilcoxon rank-sum test.
Result:
The wash from dentin samples treated with DAP and MCP showed minimal CFU counts, indicating both pastes’ high efficiency against biofilm-forming microbes from the canals of an immature tooth exhibiting pulp necrosis.
Conclusion:
Dentin samples infused with DAP and MCP showed minimal CFU counts, indicating their high effectiveness against biofilm-forming microbes from the root canal of an underdeveloped tooth with pulp necrosis. Additional research is required to validate these findings.
ObjectivesRetinal pigment epithelium (RPE) cell transplantation holds therapeutic promise for retinal degenerative diseases, but longitudinal monitoring of graft survival and efficacy remains clinically challenging. The aim of this study is to develop a simple and effective method for the therapeutic quantification of RPE cell transplantation and immune rejection in vivo.MethodsA nanoprobe was developed and modified to label donor RPE cells, and used to monitor the position and intensity of the fluorescence signal in vivo. Immunofluorescence staining and single-cell RNA sequencing (scRNA-seq) were used to characterize the cell types showing the fluorescence signal of the nanoprobe and to determine the composition of the immune microenvironment associated with subretinal transplantation.ResultsThe spatial distribution of the fluorescence signal of the nanoprobe corresponded with the site of transplantation, but the signal intensity decreased over time, while the signal distribution extended to the choroid. Additionally, the nanoprobe fluorescence signal was detected in the liver and spleen during long-term monitoring. Conversely, in mice administered the immunosuppressive drug cyclosporine A, the decrease in signal intensity was slower and the expansion of the signal distribution was less pronounced. Immunofluorescence analysis revealed a significant temporal increase in the proportion of macrophages with nanoprobe-labeled cells following transplantation. The stability and cell-penetrating ability of the nanoprobe enables the labeling of immune cell niches in RPE transplantation. Additionally, scRNA-seq analysis of nanoprobe-labeled cells identified MDK and ANXA1 signaling pathway in donor RPE cells as initiators of the immune rejection cascade, which were further amplified by macrophage-mediated pro-inflammatory signaling.ConclusionNear-infrared fluorescent nanoprobes represent a reliable method for in vivo tracing of donor RPE cells and long-term observation of nanoprobe distribution can be used to evaluate the degree of immune rejection. Molecular analysis of nanoprobe-labeled cells facilitates the characterization of the dynamic immune cell rejection niche and the landscape of donor-host interactions in RPE transplantation.
Efficient chemical kinetic model inference and application in combustion are challenging due to large ODE systems and widely separated time scales. Machine learning techniques have been proposed to streamline these models, though strong nonlinearity and numerical stiffness combined with noisy data sources make their application challenging. Here, we introduce ChemKANs, a novel neural network framework with applications both in model inference and simulation acceleration for combustion chemistry. ChemKAN's novel structure augments the generic Kolmogorov Arnold Network Ordinary Differential Equations (KAN-ODEs) with knowledge of the information flow through the relevant kinetic and thermodynamic laws. This chemistry-specific structure combined with the expressivity and rapid neural scaling of the underlying KAN-ODE algorithm instills in ChemKANs a strong inductive bias, streamlined training, and higher accuracy predictions compared to standard benchmarks, while facilitating parameter sparsity through shared information across all inputs and outputs. In a model inference investigation, we benchmark the robustness of ChemKANs to sparse data containing up to 15% added noise, and superfluously large network parameterizations. We find that ChemKANs exhibit no overfitting or model degradation in any of these training cases, demonstrating significant resilience to common deep learning failure modes. Next, we find that a remarkably parameter-lean ChemKAN (344 parameters) can accurately represent hydrogen combustion chemistry, providing a 2x acceleration over the detailed chemistry in a solver that is generalizable to larger-scale turbulent flow simulations. These demonstrations indicate the potential for ChemKANs as robust, expressive, and efficient tools for model inference and simulation acceleration for combustion physics and chemical kinetics.
Marten T. Raaphorst, Joan Enrique-Romero, Thanja Lamberts
Cyanopolyynes, a family of nitrogen containing carbon chains, are common in the interstellar medium and possibly form the backbone of species relevant to prebiotic chemistry. Following their gas phase formation, they are expected to freeze out on ice grains in cold interstellar regions. In this work we present the hydrogenation reaction network of isolated HC_{3}N, the smallest cyanopolyyne, that consists over-a-barrier radical-neutral reactions and barrierless radical-radical reactions. We employ density functional theory, coupled cluster and multiconfigurational methods to obtain activation and reaction energies for the hydrogenation network of HC_{3}N. This work explores the reaction network of the isolated molecule and constitutes a preview on the reactions occurring on the ice grain surface. We find that the reactions where the hydrogen atom adds to the carbon chain at carbon atom opposite of the cyano-group give the lowest and most narrow barriers. Subsequent hydrogenation leads to the astrochemically relevant vinyl cyanide and ethyl cyanide. Alternatively, the cyano-group can hydrogenate via radical-radical reactions, leading to the fully saturated propylamine. These results can be extrapolated to give insight into the general reactivity of carbon chains on interstellar ices.
Swarm robotics has potential for a wide variety of applications, but real-world deployments remain rare due to the difficulty of predicting emergent behaviors arising from simple local interactions. Traditional engineering approaches design controllers to achieve desired macroscopic outcomes under idealized conditions, while agent-based and artificial life studies explore emergent phenomena in a bottom-up, exploratory manner. In this work, we introduce Analytical Swarm Chemistry, a framework that integrates concepts from engineering, agent-based and artificial life research, and chemistry. This framework combines macrostate definitions with phase diagram analysis to systematically explore how swarm parameters influence emergent behavior. Inspired by concepts from chemistry, the framework treats parameters like thermodynamic variables, enabling visualization of regions in parameter space that give rise to specific behaviors. Applying this framework to agents with minimally viable capabilities, we identify sufficient conditions for behaviors such as milling and diffusion and uncover regions of the parameter space that reliably produce these behaviors. Preliminary validation on real robots demonstrates that these regions correspond to observable behaviors in practice. By providing a principled, interpretable approach, this framework lays the groundwork for predictable and reliable emergent behavior in real-world swarm systems.
This article addresses calibration challenges in analytical chemistry by employing a random-effects calibration curve model and its generalizations to capture variability in analyte concentrations. The model is motivated by specific issues in analytical chemistry, where measurement errors remain constant at low concentrations but increase proportionally as concentrations rise. To account for this, the model permits the parameters of the calibration curve, which relate instrument responses to true concentrations, to vary across different laboratories, thereby reflecting real-world variability in measurement processes. Traditional large-sample interval estimation methods are inadequate for small samples, leading to the use of an alternative approach, namely the fiducial approach. The calibration curve that accurately captures the heteroscedastic nature of the data, results in more reliable estimates across diverse laboratory conditions. It turns out that the fiducial approach, when used to construct a confidence interval for an unknown concentration, produces a slightly wider width while achieving the desired coverage probability. Applications considered include the determination of the presence of an analyte and the interval estimation of an unknown true analyte concentration. The proposed method is demonstrated for both simulated and real interlaboratory data, including examples involving copper and cadmium in distilled water.
Mohammad Rehan Asad, Ritu Kumar Ahmad, Husam A. Almalki
et al.
Background:
This study aimed to demonstrate the association between smartphone use and De Quervain’s syndrome in Saudi Arabian teenagers, as well as to establish the length of phone use among these patients in order to evaluate whether it was connected to the emergence of De Quervain’s tenosynovitis.
Methodology:
This cross-sectional observational study was conducted among teenagers in Saudi Arabia studying in public and private schools. Most of the inquiries were closed-ended and sought information regarding the students’ use of various mobile phone sizes, regular text messaging, discomfort in the wrist or thumb, swelling or a snapping sound over the thumb, and limitations or pain aggravation when handling various objects.
Results:
The total number of teenagers that participated in the study was 200; 111 (55.5%) of them were males, while the remaining 89 (44.5%) were females. Out of 200 participants, 135 (67.5%) tested positive for Finkelstein disease; of these, 21 (15.5%) used smartphones for under 4 hours, 53 (39.2%) used smartphones for 5–7 hours, and 21 (15.5%) used smartphones more than 10 hours with P value of 0.008, which is significant and hence concluded that duration of using mobile phones had impact on De Quervain’s disease.
Conclusion:
In conclusion, Finkelstein’s sign, a marker that De Quervain’s illness is widespread, was present in 67.5% of the patients. Current findings suggest that De Quervain cannot be completely ruled out because of this population’s propensity to develop it.