Central Scientific Instruments Organisation

The remarkable adaptability of chromogenic phases found in polymers like conjugated polydiacetylene (PDA) is especially noteworthy due to their vital role in durable sensing applications. This study presents the synthesis and utilization of polymers and PDA‐based functionalized dyes as a visual color indicator. These dyes are developed by modifying the head groups of 10,12‐tricosadiynoic acid (TC) with aromatic alcohols, employing Steglich esterification. Confirmation of the functionalized dye derivative is achieved using various analytical techniques. In addition, the composite film is fabricated by mixing a modified monomer dye with the polymer polyvinyl alcohol (PVA). The composite film is exposed to UV radiation for topochemical polymerization in order to form PDA. Moreover, the functionalized dyes demonstrate an irreversible color transition (above 10 °C) in thermochromic investigations. The present study revealed that these conjugated PDA‐based thermochromic composite films showcased an irreversible color at low temperatures, which can serve as warning indicators for low‐temperature sensitive milk packets. Any change in the indicator's color by means of storage conditions may help the retailers and end‐users to accept the packet further.

Visual-inertial odometry (VIO) plays a prominent role in computer vision, robotics, and augmented and virtual reality applications to estimate the pose and velocity of an agent. This paper focuses on an important aspect of the recently used optimization-based VIO architectures, that is, the optimization window length. This window length is generally fixed arbitrarily by the users, and there is a strong need for its adaptation as not one window size will suit the entire trajectory. In this work, two different statistical measures, one based on change point detection and the other based on standard deviation, are proposed as the means for adapting the optimization window length. This adaptive windowing approach has shown promising results and is benchmarked against the ground truth and the contemporary approach with fixed window length. The experiments conducted on the publicly available EuRoC MAV dataset and the TUM-VI dataset demonstrate the effectiveness of the proposed approach.

The Afghanistan earthquake of 21 June 2022 ruptured a ~10 km-long fault segment in the North Waziristan–Bannu fault system (NWBFS) located towards the north of the Katawaz Basin. The earthquake was shallow and reportedly caused widespread devastation. In this article, we investigated the long-term, i.e., geological and geomorphological, evidence of deformation along the earthquake segment. For comparison, we also studied the short-term space geodetic and remote sensing results documenting a visible offset between the fault traces. Focusing on the fault modelling and on the published results, it is thus clear that the earthquake rupture did not reach the surface; instead, it stopped in the shallow sub-surface at ~1 km depth. Moreover, the InSAR analyses show some technical issues, such as coherence loss, etc., likely due to severe ground-shaking leaving some gaps in the results; geological and geomorphological evidence complemented this information. As an outcome of this research, we confirmed that InSAR results could generally capture the overall fault geometry at depth, even in cases of blind faulting, whereas the detailed geometry of the tectonic structure, in this case with a right stepping en-echelon pattern, could be successfully captured by combining it with geological and geomorphological approaches and optical remote sensing observations. Accordingly, the right stepping fault generates a restraining bend in the dominantly left-lateral shear zone. Therefore, such fault stepovers are capable of localizing strain and could act as loci for seismic ruptures, bearing strong implications for the seismic hazard assessment of the region, as well as of other strike-slip fault zones.

The growing demand for sustainable energy solutions has intensified interest in hydrogen as a clean and efficient energy carrier. Among the diverse materials studied for hydrogen generation, MXenes two-dimensional transition metal carbides, nitrides, and carbonitrides stand out due to their distinctive physicochemical properties, including tunable surface terminations, high hydrophilicity, and excellent metallic conductivity. As non-noble metal-based catalysts, MXenes offer scalability and significant potential for integration into electrochemical (EC) and photoelectrochemical (PEC) water splitting, particularly as catalysts for the hydrogen evolution reaction and oxygen evolution reaction. This mini-review delves into recent advancements in engineered MXenes for hydrogen generation, emphasizing strategies such as innovative surface engineering, defect modification, heterostructure formation, heteroatom doping, and hybridization to enhance their performance in EC and PEC applications. Furthermore, it explores challenges and insights associated with the development of MXene-based hybrid materials for efficient water splitting, facilitating the sustainable production of solar fuels. By addressing critical issues like material stability, synthesis scalability, and practical system integration, this review underscores the transformative potential of MXenes as frontier materials for next-generation hydrogen technologies, contributing to global energy transition initiatives.

Visible photocatalytic oxidation has emerged as a promising approach for sustainable environmental remediation, leveraging sunlight to drive the degradation of organic pollutants. This review examines the principles and mechanisms underlying visible light photocatalysis, focusing on the development of advanced photocatalytic materials that enhance efficiency under solar irradiation. Key strategies, including the design of heterojunctions, doping with metal and nonmetal elements, and the synthesis of hybrid materials, are explored to improve photocatalytic activity and broaden the light absorption spectrum. The application of visible light photocatalysts in the degradation of various environmental contaminants such as VOCs is critically analyzed, highlighting their potential in addressing environmental challenges. Furthermore, this review discusses the scalability and practical implementation of visible photocatalytic systems, underscoring their role in promoting sustainable practices and achieving clean water and air quality goals. The findings suggest that visible photocatalytic oxidation is a vital tool for advancing sustainable environmental remediation technologies.

Recent developments in image analysis and interpretation using computer vision techniques have shown potential for novel applications in microbiology laboratories to support the task of automation, aiming for faster and more reliable detection. Image processing techniques and machine learning models can be valuable tools in the screening process, helping technicians spend less time classifying no-growth results and quickly separating the categories for further analysis. In this context, creating a dataset of different bacterial strain images is a fundamental objective for developing and improving the accuracy of image processing models. Therefore, this manuscript acquired a dataset of water samples with different bacterial strain images on a petri dish following a standardized process with controlled conditions of positioning and lighting. The image acquisition device was also developed with a light-emitting diode (LED) and diffuser as a lighting source and a smartphone camera with 16 MP resolution. In addition, the present manuscript also focuses on comparing the accuracy of the proposed algorithm with the available apps and software using the custom-built imaging device. Hence, the resulting dataset consists of 100 images, which is helpful for researchers working in image processing to develop an algorithm for automated counting of bacterial colonies on petri dishes. Graphical Abstract

The landscape of postdoctoral research in India is characterized by both opportunities and challenges. Over time, significant evolution has been witnessed, supported by diverse funding agencies and academic institutions. However, postdoctoral fellows face various hurdles, including limited fellowship durations, administrative obstacles, and uncertainties in the job market. To gain comprehensive insights into these experiences, a nationwide survey was conducted by the Indian National Young Academy of Sciences (INYAS), gathering responses from 189 postdoctoral researchers. The survey illuminated the motivations, aspirations, and concerns of postdoctoral fellows in India. While many respondents pursue postdoctoral positions to gain research experience and enhance their skill sets, they encounter challenges in accessing state-of-the-art facilities and securing job opportunities post-training. Moreover, preferences for postdoctoral destinations and funding agencies underscore the need for increased awareness and streamlined application processes. Emphasizing job security, research skills development, and career advancement, the survey highlights the priorities and expectations of postdoctoral researchers. In response to these findings, a set of recommendations is proposed to address existing challenges and enhance the postdoctoral experience in India. These include increasing fellowship numbers and durations, fostering inter-institutional collaborations, minimizing biases in recruitment, and supporting career development, particularly for women researchers. Implementation of these recommendations can contribute to building a more robust and supportive research ecosystem, promoting talent growth and retention within the country. This endeavor aims to contribute to ongoing discussions regarding the enhancement of the postdoctoral experience and the advancement of research endeavors in India.

Shape memory alloy (SMA)-based actuators are quite commonly used in conjunction with flexible manipulators and find great potential in medical applications due to their excellent biocompatibility, decent maintainability, and ease in actuation through Joule heating. One of the potential applications of SMA-based flexible manipulators is the optical irradiation of superficial (surface) tumors for plasmonic photothermal therapeutics. This requires manipulating an optical fiber bundle to irradiate the tumor surface with near-infrared radiation. Motivated by this novel application, this paper proposes a working prototype of a flexible tube manipulator that demonstrates its capability to follow the trajectories of a superficial tumor for its irradiation. The flexible manipulator’s design mechanics are based on a constant curvature assumption. The tracking capabilities are realized through a compatible robust controller to actuate two pairs of antagonistically arranged SMA springs. Trajectory tracking performance of the proposed manipulator is verified through experiments. Compared to the earlier works, the SMA constitutive model with memory parameters has been considered in this work while developing the mathematical model that supports the inherent path dependence capabilities of SMA. The experimental results confirm that the proposed manipulator can track the commonly encountered 2D tumor trajectories like circle, oval and concentric circles with a maximum positioning error of only 7.2%. On the other hand, the most difficult 2D trajectory such as an irregular trajectory that includes multiple sharp turns is traceable by the developed manipulator with a maximum positioning error of 15%. The reasonable accuracy of the developed manipulator confirms the applicability of the proposed concept for tumor irradiation. Compared to other similar work with continuum manipulators, our results report tracking of a few new trajectories like concentric and irregular trajectories which confirm the applicability of the antagonistic SMA actuator for tumor irradiation and allied applications.

Correction for ‘Analyzing the charge contributions of metal–organic framework derived nanosized cobalt nitride/carbon composites in asymmetrical supercapacitors’ by Vishal Shrivastav et al. , Nanoscale Adv. , 2024, 6 , 4219–4229, https://doi.org/10.1039/D4NA00291A.

In this study, we conduct a quantitative analysis of the interferometric-based phase-sensitive interrogation principle for πFBG sensors using numerical simulations. The path imbalance of the interferometer arms and the line-shape function of the light signal from the πFBG sensor are critical factors influencing the sensitivity and dynamic range characteristics of the interferometric interrogation principle. Our analysis reveals several unique response characteristics that will be valuable for designing and optimizing interferometric interrogation principle using πFBGs as sensing elements. The simulation results are validated through experimental studies on πFBG sensors with in-house fabricated fiber-based Mach–Zehnder interferometers, which have path length differences of 10 mm, 19.40 mm, 28.90 mm, and 37.10 mm, respectively.

Apnea detection is a significant health concern due to its potential consequences, ranging from increased blood pressure to heart failure. Polysomnography is currently the gold standard for identifying apnea patterns during sleep. However, it requires trained personnel for analysis and is not suitable for long-term monitoring due to discomfort. To address these limitations, this paper proposes a contactless approach for apnea detection. The proposed approach utilizes visible and thermal imaging to remotely measure the breathing signal. This signal is then fed into deep learning models, including a 1-dimensional convolutional neural network (CNN), a long short-term memory (LSTM) network, and a hybrid model combining both. The effectiveness of these models is evaluated through comparative analysis. To evaluate the performance of the models, the authors define an apnea index to assess the presence of apnea in per second overlapped epochs. The validation of the contactless approach is evaluated by comparing the apnea detection results with those obtained from a contact-based breathing signal. The results demonstrate promising performance for each model. The mean absolute error values are reported as 0.6195 for CNN, 1.0177 for LSTM, and 1.3540 for CNN–LSTM. The Bland–Altman and correlation plot analyses demonstrate a high level of agreement between the contactless approach and the traditional contact-based method. Consequently, this approach might be useful for applications such as home-based patient monitoring, sleep studies, and neonatal apnea detection.

The primary objective of this study was to investigate the feasibility of magnetic resonance imaging (MRI) usage over computed tomography (CT) to perform three-dimensional (3D) cephalometric analyses. The secondary objective is to find intra- and interobserver reliability of manual cephalometric landmarks identification in both CT and MRI scan data. Data from 40 patients were used in this study, with orthodontists manually identifying 37 landmarks on both CT and MRI scans. The interclass correlation coefficient (ICC) was calculated individually for both CT and MRI scan data to find intra- and interobserver reliability. In addition to ICC, paired t‑test and mean error were also calculated. Ground truth landmarks were calculated by considering the mean values of manually located 37 landmarks by observers for both CT and MRI. Thirty-seven cephalometric measurements (29 linear, 6 angular, and 2 ratios) were measured using 37 ground truth landmarks. Mean error (ME) between CT and MRI measurements was calculated and paired t‑test was performed to find the reliability of MRI usage over CT. Bland–Altman analysis was also performed on the measurements to check the agreement between CT and MRI. The intra- and interobserver reliability was found to be reliable (ICC > 0.98, and P > 0.05) for all 37 landmarks in both CT and MRI. The ME for linear measurements was found to be 1.81 mm for hard tissue, 1.72 mm for soft tissue, and 1.53° for hard tissue angular measurements between CT and MRI. The paired t‑test performed on measurements between CT and MRI proved to be statistically insignificant (p > 0.05). The Bland–Altman analysis also showed strong agreement and low systemic bias between CT and MRI data. The strong ICC and P values shows the high reliability and reproducibility of manual landmark identification on both CT and MRI. The ME for the linear and angular measurements between CT and MRI was found to be well within acceptable limits. The results of paired t‑test and Bland–Altman analyses for cephalometric measurements between CT and MRI has shown strong evidence supporting the use of MRI as a substitute for CT.

Several drawbacks of 2D radiography have been addressed by the use of 3D modalities such as CT/CBCT. However, these are associated with significantly increased radiation exposure. Hence, the role of MRI in routine orthodontic planning needs to be further investigated. The objective of the work is to evaluate feasibility of plotting cephalometric landmarks on MRI scans. 40 MRI images were collected randomly and retrospectively irrespective of age, sex, and ethnicity. For plotting purposes, 41 landmarks were found for every anonymized image separately. Two experienced orthodontists completed landmark plotting at two different time intervals. The mean error of landmark plotting, standard deviation, correlation coefficients and t-test were computed to assess the feasibility of landmark plotting on the three axes. The correlation coefficient of 0.9 indicated strong inter-observer reliability for each of the 41 landmarks on the x, y, and z axes. The paired t-test for each landmark revealed negligible differences in the orientation of the volume-rendered images. Out of all the landmarks, Porion and Orbitale had the most localization errors. MRI is a reliable tool for plotting cephalometric landmarks and thus for conducting cephalometric analyses. The landmarks' anatomical placements on the midline, bilateral, and curved structures affect how consistently they are identified. The radiation exposure to the patient for the acquisition of CT/CBCT images can be eliminated for performing 3D cephalometric analysis.

Background: Accurate segmentation of the left ventricle in echocardiograms is crucial for the diagnosis and monitoring of cardiovascular diseases. However, this process is hindered by the limited availability of high-quality annotated datasets and the inherent complexities of echocardiogram images. Traditional methods often struggle to generalize across varying image qualities and conditions, necessitating a more robust solution. Objectives: This study aims to enhance left ventricular segmentation in echocardiograms by developing a framework that integrates Generative Adversarial Networks (GANs) for synthetic data augmentation with a MultiResUNet architecture, providing a more accurate and reliable segmentation method. Methods: We propose a GAN-based framework that generates synthetic echocardiogram images and their corresponding segmentation masks, augmenting the available training data. The synthetic data, along with real echocardiograms from the EchoNet-Dynamic dataset, were used to train the MultiResUNet architecture. MultiResUNet incorporates multi-resolution blocks, residual connections, and attention mechanisms to effectively capture fine details at multiple scales. Additional enhancements include atrous spatial pyramid pooling (ASPP) and scaled exponential linear units (SELUs) to further improve segmentation accuracy. Results: The proposed approach significantly outperforms existing methods, achieving a Dice Similarity Coefficient of 95.68% and an Intersection over Union (IoU) of 91.62%. This represents improvements of 2.58% in Dice and 4.84% in IoU over previous segmentation techniques, demonstrating the effectiveness of GAN-based augmentation in overcoming data scarcity and improving segmentation performance. Conclusions: The integration of GAN-generated synthetic data and the MultiResUNet architecture provides a robust and accurate solution for left ventricular segmentation in echocardiograms. This approach has the potential to enhance clinical decision-making in cardiovascular medicine by improving the accuracy of automated diagnostic tools, even in the presence of limited and complex training data.

A fluorescent detection platform was designed using boric acid-functionalized terbium metal–organic framework (BA-Tb-MOF) and carboxyl-modified magnetic nanoparticles (MNPs) to identify Salmonella typhimurium (S. typhimurium) bacteria. Firstly, carboxyl-modified Fe3O4MNPs were coated with specific aptamer (Apt-MNPs) as the capture probe for S. typhimurium. Then, the Apt-MNPs were added to the bacterial suspension to facilitate the targeted binding. Subsequently, the fluorescent probe (BA-Tb-MOF) was introduced into this solution. The BA-Tb-MOF was strongly attached to the bacterial surface through interactions between BA and glycolipids on the bacterial cell walls, forming a stable complex. As the bacterial concentration increased, the fluorescence intensity of the solution progressively decreased due to the binding and removal of bacteria-Apt-MNPs/BA-Tb-MOF complexes through magnetic separation. Under optimum conditions, the concentration of S. typhimurium and the fluorescence intensity showed an inverse linear relationship within the range of 10¹–10⁹ CFU/mL, and the detection limit was 4 CFU/mL. The developed sensor showed high specificity against several other pathogenic bacteria such as E. coli, S. aureus, and P. aeruginosa. The developed fluorescence platform also successfully detected the S. typhimurium in drinking water and egg samples with satisfactory recoveries (83–98%). This strategy can be investigated further for the detection of S. typhimurium and other pathogens in food and clinical samples. Graphical Abstract

The detection of organic pollutants at ultra-low concentrations is crucial for environmental monitoring, yet existing surface-enhanced Raman scattering (SERS) platforms often suffer from limited sensitivity, poor stability, and inconsistent signal reproducibility. To address these challenges, this study presents a high-performance SERS platform based on in situ gold (Au) nanoparticle-engineered Ti3C2Tx MXenes. This novel approach enhances signal amplification and ensures long-term stability for pollutant detection. The platform achieves an exceptional limit of detection (LOD) of 10⁻¹¹ M with an enhancement factor of 10¹⁰ for Methylene Blue (MB), demonstrating its superior sensitivity. Additionally, signal repeatability has been validated by calculating the relative standard deviation (RSD), and the SERS substrate retains 83% of its signal intensity after 5 months of storage, confirming its durability. Furthermore, the platform effectively detects polybrominated diphenyl ether (BDE-47), a persistent organic pollutant, at concentrations below the regulatory threshold of 10⁻⁶ M. These results highlight the potential of the proposed SERS platform for reliable and long-term environmental monitoring of hazardous substances. Graphical Abstract

Photonic nanojet (PNJ) of subwavelength spot-size originating at shadow side of a microsphere is projected as an imperative optical tool for micro-nano optics applications. Lack of a free hold PNJ source limits its vast potential. A state-of-the-art PNJ is introduced on a chemically etched encaved optical fiber nanoprobe holding a microsphere. Nanoprobe generating optical beam of spot-size ~2 μm, is focused on the microsphere resulting in PNJ of varying spot-size, 0.8λ to 0.5λ, over its’ length ~13λ at 660 nm wavelength. A PNJ centered around 840 nm wavelength combined with an effective numerical aperture ~2 results in an en-face optical coherence tomography (OCT) image of lateral resolution ~247 nm and phase map with optical thickness, ~70 nm for a standard DVD. Further, the PNJ based backscattered nanoscopy of silver nano-particles is exhibited with resolution ~40 nm. This commercially viable PNJ head can be an ideal platform for various microscopies and other applications.

The application of deep-learning techniques to aroma chemicals has resulted in models that surpass those of human experts in predicting olfactory qualities. However, public research in this field has been limited to predicting the qualities of individual molecules, whereas in industry, perfumers and food scientists are often more concerned with blends of multiple molecules. In this paper, we apply both established and novel approaches to a data set we compiled, which consists of labeled pairs of molecules. We present graph neural network models that accurately predict the olfactory qualities emerging from blends of aroma chemicals along with an analysis of how variations in model architecture can significantly impact predictive performance.

3D (three-dimensional) food printing has emerged as a transformative technology, offering exceptional adaptability and customization across various industries. This review explores its potential to enhance environmental sustainability by minimizing food waste, improving portion control, and promoting eco-friendly practices. Key technological foundations, such as rheological assessments for material flow optimization, colorimetric analysis for accurate color representation, and advanced material handling techniques for consistent texture and nutrition, are critically examined. Moreover, it highlights the synergy between mechanical precision, algorithmic control, and material science, illustrating how these elements collectively ensure adherence to high-quality and safety standards in food engineering. The study also reviews 3D food printing technologies, methods, materials, and applications, emphasizing the pivotal role of customization in addressing diverse dietary, cultural, medical, and aerospace needs. A SWOT (strengths, weaknesses, opportunities, and threats) analysis evaluates the current capabilities and limitations of the technology, identifying challenges and future prospects. This comprehensive analysis underscores the potential of 3D food printing to revolutionize food production, offering valuable insights for researchers, practitioners, and policymakers.