Fe-based spin crossover (SCO) complexes are a highly versatile family of materials, especially for room-temperature device applications. In addition to their well-documented applications, this class of systems is now theorized to harbor both small and large polarons. Nevertheless, none of their promising applications can be truly realized until a solid understanding of their respective surfaces is obtained. In this Perspective, recent efforts (i) providing insights into the surface termination of some of the well-studied Fe-based SCO materials, and (ii) exploring the possibility of polaronic transport in these complexes, are discussed. Finally, the use of synchrotron-based vacuum ultraviolet (VUV) radiation in order to detect the possible surface polarons in these systems is also proposed. The discussions contained herein are therefore expected to catalyze future investigations into the fabrication of room-temperature nanodevices that exploit polaronic transport in these complexes.
There has been an increase in plastic production and consumption in recent years, which has become a major global environmental challenge due to the persistence of plastics in nature. Microplastics (MPs) contamination signifies a serious environmental challenge, yet discrepancies among analytical methodologies and sample matrices continue to limit the harmonization and cross-comparability of reported data. This study investigates MPs’ extractions from the soil collected from the Sukhna Lake Watershed, Chandigarh, India. This research employs systematic sampling for soil collection by dividing a single sampling site into a 4 × 4 grid. Furthermore, the density separation method was employed for the extraction of MPs from the soil samples. The extracted MPs were comprehensively characterized using a fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscope (FE-SEM), energy-dispersive X-ray Spectroscopy (EDS)-mapping, X-ray photoelectron spectroscopy (XPS), and inductively coupled plasma mass spectrometry (ICP-MS), which was used to elucidate the molecular structure, morphology, surface chemistry, and elemental composition of the MPs. The MPs were further solubilized in various solvents and analyzed using UV-visible spectroscopy. The results from the FTIR and FE-SEM demonstrated the presence of various types of plastics at the collecting site, including polyethylene, polypropylene, polyvinyl chloride, and polyethylene terephthalate in different morphologies. These techniques also confirmed that plastics had undergone prolonged interactions with the environment, enabling them to interact with contaminants. Furthermore, the results from XPS, ICP-MS and UV-Visible spectroscopy suggested the presence of organic and inorganic contaminants, including heavy metals, in the MPs. The combined results emphasize that the MPs present in the watershed can function as potential vectors for the accumulation and transport of potent and persistent contaminants. This research delivers new insights into MPs as environmentally aged, oxidized materials with mineral-organic surface coatings, highlighting their enhanced capacity to act as carriers of pollutants and their broader environmental implications.
Area-dependent memristive devices based on the valence change mechanism are promising candidates for emerging analog and neuromorphic computing due to their intrinsic analog switching behavior and reduced variability. Among these, IGZO-based devices are particularly attractive owing to their potential for multifunctional applications, including optoelectronics and flexible electronics. However, systematic design strategies that directly link the material and interface properties to their device performance remain unexplored. In this work, we demonstrate that the switching polarity and electrical characteristics of IGZO-based memristive devices can be systematically tuned by modifying the top electrode. This control is shown to originate from changes in the band alignment and the resulting spatial electric field distribution across the device. To identify the governing interface and underlying transport mechanisms, we combine energy band diagram simulations with XPS-based band alignment measurements. Using this experimentally validated framework, the measured I–V characteristics are quantitatively reproduced within the Tsu–Esaki tunneling model. These results establish a consistent physical understanding of switching in IGZO-based devices and demonstrate that band engineering provides a powerful route to control both transport and switching behavior, enabling targeted optimization for large-scale analog and neuromorphic systems.
Jeffery–Hamel (JH) flow explains fluid motion between two intersecting walls, generating either a converging or diverging channel, and it is a reliable model for simulating blood flow behavior through arteries. The flow dynamics and heat transfer properties of nanofluids are closely studied by evaluating the combined influence of the magnetic field intensity, porous medium porosity, nanoparticle volume fraction, and Prandtl number. Suitable similarity transformations are applied to the governing momentum and energy equations, sequentially converting them to a dynamic system of non-linear ordinary differential equations. The resultant coupled equations are computed using the Bernstein collocation method (BCM), and the results are compared with those of the Chebyshev collocation method (CCM). The significance of nanoparticles on fluid characteristics are analyzed through different thermo-physical properties by dispersing two different particles, Fe3O4 and Cu, on the base fluid blood. The effect of various governing factors on the velocity and temperature profile is closely evaluated, and the skin friction coefficient and Nusselt number are calculated to determine the flow resistance and heat transmission characteristics. Moreover, the computed numerical values of convergence and absolute error assures that the accuracy of the utilized method of BCM are verified and validated via response surface methodology (RSM) and propounds the efficacy in offering the reliable numerical solution to the JH nanofluid flow problem.
This paper presents the design and hardware validation of a CMOS–memristor hybrid neural signal processing platform. The system combines a CMOS neural amplifier, a level-crossing spike encoder, FPGA-based pulse conditioning, and a volatile TiOx memristive integrating sensor (MIS) to convert low-frequency neural activity into spike-based events and then into a compact resistance trajectory. In this study, previously recorded LFP data were preprocessed and replayed through the hardware signal chain, while saline-bath electrode coupling was used as a controlled intermediate validation step between direct electrical injection and future biological acquisition. The results show that the front-end can preserve event timing under electrode-mediated coupling and that temporally clustered spike activity can induce measurable MIS resistance modulation. Overall, the platform demonstrates a feasibility-oriented hardware pathway for sensor-proximal neural activity summarisation, while quantitative event-detection benchmarking, multi-device statistics, and multi-channel MIS validation remain necessary for future deployment-oriented studies.
Microbial-mediated green synthesis of silver nanoparticles (AgNPs) represents an eco-friendly alternative to conventional chemical methods by utilising biological reducing and stabilising agents. In this study, gut bacteria isolated from sewage-exposed fish were used to synthesise AgNPs, and the resulting nanoparticles were evaluated for their antimicrobial and antioxidant activities. Bacterial biomass was used to reduce Ag+ ions from a 1 mM AgNO3 solution. Nanoparticle formation and characterisation were confirmed using UV–Visible spectroscopy, Field Emission Scanning Electron Microscopy (FE-SEM), Transmission Electron Microscopy (TEM), and X-ray Diffraction (XRD). A distinct color change from pale yellow to brown and a characteristic absorption peak at approximately 350–400 nm confirmed AgNP synthesis. TEM analysis revealed predominantly spherical nanoparticles ranging from 10 to 30 nm in size. The biosynthesised AgNPs exhibited antibacterial activity against both Gram-positive and Gram-negative bacteria including AmpC-producing Escherichia coli, producing inhibition zones in the range of 24–31 mm. The synthesised nanoparticles displayed strong dose-dependent antioxidant activity, in terms of DPPH and ABTS+ radical scavenging activity, with IC50 of 18 μg/mL and 39 μg/mL, respectively. These findings indicate the potential of sewage fish gut bacteria as a sustainable source for producing bioactive AgNPs with promising applications as an antibacterial agent.
Soil health is a critical determinant of sustainable agriculture and ecosystem stability, yet conventional soil analysis methods remain labor-intensive and unsuitable for real-time monitoring. Nanobiosensors have emerged as advanced tools enabling rapid, sensitive, and on-site detection of key soil parameters. This review provides a comprehensive evaluation of nanobiosensor technologies, including electrochemical, optical, piezoelectric, and magnetic sensors, along with enzyme-, DNA and cell-based systems. Their underlying sensing mechanisms, such as signal transduction through electrical, optical, and mechanical changes, are discussed in detail. The applications of nanobiosensors in monitoring soil nutrients, heavy metals, microbial activity, moisture, and plant health are critically analyzed, highlighting their role in precision agriculture. Integration with Internet of Things (IoT)-based platforms for real-time and data-driven decision-making is also emphasized. Key insights reveal that while nanobiosensors offer high sensitivity, selectivity, and rapid response, challenges related to stability, calibration, and large-scale field deployment persist. Future research should focus on improving sensor durability, standardization, and cost-effective scalability for practical agricultural implementation. This review advances current understanding of nanobiosensor technologies and their potential to transform next-generation soil and plant health monitoring systems.
Linear programming (LP) is among the most fundamental optimization techniques. However, solving LP problems on conventional digital hardware is increasingly constrained by the polynomial computational complexity of matrix operations. In this work, we present an analog matrix computing (AMC) circuit built on resistive random-access memory (RRAM) crossbar arrays and the projection neural network (PNN) model that solves LP problems in one step. The proposed circuit directly maps the PNN dynamical system onto a closed-loop feedback system, where an RRAM-based projection matrix computation unit executes the projection matrix computation in one step, while analog neuron modules perform integration, nonlinear activation, and subtraction to establish a global negative feedback loop. The proposed circuit was tested on assignment problems and instances from the LP Netlib test set. Circuit-level simulations show that the solver reaches stable solutions within approximately 15 μs for problems with up to 100 variables, achieving nearly two orders of magnitude speedup over the digital PNN baseline. We further introduce an analog-digital hybrid approach where the analog circuit rapidly supplies a near-optimal seed solution to initialize a digital iterative solver, reducing subsequent iteration counts by up to 54.4%. These results demonstrate that RRAM-based AMC offers a promising route toward real-time, energy-efficient LP solving at scale.
IntroductionIn 2022, female breast cancer ranked as the second most prevalent disease worldwide. Present medications have significant adverse effects that limit their application. Quinoa seed oil (QSO) has an effective role in suppressing cancer, however, their low solubility in aqueous solution has limited their use. The integration of QSO into nanoparticles can enhance their therapeutic efficacy. This study aimed to prepare QSO-loaded bovine serum albumin (BSA) nanoparticles conjugated with folic acid (FA) (FA-QSO-BSA NPs) and evaluate their anticancer activity on breast cancer cells (MCF-7 and MDA-MB-231).MethodsQSO extraction was carried out using the Soxhlet extraction technique. FA-QSO-BSA NPs were formulated by the desolvation method. The NPs were characterized using DLS, FTIR, and HPLC techniques. Drug encapsulation efficiency and loading capacity were determined by a UV-visible spectrophotometer. The cytotoxic effects of FA-QSO-BSA NPs were investigated by MTT, flow cytometry, and confocal microscopy.ResultsThe results showed the spherical morphology of FA-QSO-BSA NPs with an average size of 146.4 ± 1.32 nm, a polydispersity index of 0.214 ± 6.1, and a zeta potential of −26.0 ± 5.43 mV. The cytotoxicity effects of FA-QSO-BSA NPs against breast cancer cells were in a concentration-dependent manner. At IC50 concentrations (MCF7 = 64.12 μg/mL and MDA-MB-231 = 79.14 μg/mL), FA-QSO-BSA NPs strongly induced nucleus morphological changes, reduced cell proliferation, increased phosphatidylserine exposure, and arrested the cell cycle at the G0/G1 phase.DiscussionThe increased cytotoxicity of FA-QSO-BSA NPs against breast cancer cells, together with their ability to induce apoptosis and cell cycle arrest, underscores their promise as a strong anticancer drug.
The emergence of biodegradable and bioresorbable devices is currently transforming the field of implantable neurotechnology by enabling transient, biocompatible systems that avoid the risks and complications associated with permanent implants. This review explores the development of transient “green” electronics and their shift into medical applications, emphasizing their potential to revolutionize neural interfaces. A detailed overview of transient, flexible neural probes, both for the central and peripheral nervous systems, for recording and stimulation, highlights their utility in neurophysiology, neuromodulation, and neuroprosthetics. Additionally, we examine emerging strategies for transient power supplies, including energy harvesting techniques and wireless power transfer, essential for device functionality in implantable settings. Critical considerations such as biocompatibility, safety, and clinical implications are discussed, focusing on the physiological response to implanted bioresorbable materials. This study reviews and analyzes the advantages, challenges, and opportunities posed by biodegradable neurotechnology, including its potential to minimize surgical interventions and reduce long-term complications. Finally, we offer recommendations for future research and clinical translation, identifying key areas for innovation in bioresorbable neurotechnology.
This study reports a surface plasmon resonance (SPR) sensor concept for detecting heavy metal ions in water using a cost-effective multilayer stack based on aluminum (Al), aluminum oxide (Al2O3), and 2D nanomaterials. The architecture is numerically analyzed using the transfer matrix method (TMM) under TM-polarized illumination at 633 nm. Key design parameters, including prism material, Al and Al2O3 thicknesses, and 2D nanomaterial coatings (GO, rGO, sSWCNT, and graphene), are optimized to improve sensitivity and resonance definition. CaF2 is identified as the optimal prism, and an Al thickness of 40–45 nm combined with a 6 nm Al2O3 layer provides a favorable trade-off between resonance sharpness and field confinement. Among the evaluated coatings, rGO yields the best overall performance. For Pb2+ sensing, the optimized configuration achieves an angular sensitivity of 195.67°/RIU, a detection accuracy (DA) of 0.334, and a figure of merit (FoM) of 481.62 RIU−1. Electric-field analysis indicates strong confinement at the sensing interface. Overall, the proposed Al–Al2O3/2D SPR platform supports real-time, label-free detection of toxic metal ions and highlights aluminum-based stacks as a scalable alternative to noble-metal configurations for environmental sensing.
IntroductionThe exponential growth of data-intensive workloads, spanning large-scale neural inference and scientific computing, has exposed the inherent bottlenecks of conventional von Neumann architecture. In-memory analog computing, which leverages memristive crossbar arrays, has emerged as a compelling alternative by enabling highly parallel matrix computations through the exploitation of fundamental physical laws. However, due to the intrinsic stochasticity of resistive switching dynamics, achieving high-precision analog programming becomes difficult and mandates iterative write-verify procedures. These digitally-controlled loops introduce substantial latency and peripheral hardware overhead, undermining the throughput and energy efficiency inherent to analog acceleration.MethodsWe introduce a novel closed-loop feedback architecture that transforms analog-state programming into a self-regulated physical evolution. Unlike traditional discrete control loops, the proposed circuit utilizes its intrinsic dynamics to continuously sense the discrepancy between the instantaneous device conductance and a predefined target value. This error is converted in real time into a regulated feedback signal that drives the device toward the desired state, automatically halting the programming stimulus once the target is reached.ResultsBased on the fabricated memristor devices, experiment results show the circuit can achieve analog programming in one step (∼100 ns). Experimental results also validate successful 3-bit analog tuning within 100 ns, regardless of the initial conductance state. The average relative programming error is only about 2.2%. Moreover, a hybrid approach that augments this autonomous feedback with traditional write-verify cycles is adopted to enhance the programming precision. This approach improves overall programming speed by 3.7× compared to the traditional write-verify scheme.ConclusionThis work significantly improves the analog programming speed of memristor devices and offers a critical advancement for leveraging resistive memory in data-intensive storage-class applications and AI hardware.
Neurodegenerative diseases are heterogeneous neurological disorders, which represent an alarming global health concern due to their delayed diagnosis, inadequate accessibility of potential biomarkers and permanent neuronal loss. Recent development of nanodiagnostic platforms bestows transformative potential in bridging such diagnostic gaps via ultra-sensitive, real-time detection of molecular and cellular imperfections preceding the clinical onset of such disorders. This manuscript summarizes the evolving landscape of nano-neuro nexus, in which the connexion between nanotechnology and neuroscience with precise restructuring of smart nanoprobes, nanosensors and bio-responsive systems is redefining the holistic capability to visualize and measure “silent signals” responsible for the characteristic pathological progression of neurodegenerative condition/s. The importance of such platforms is credited for the current advancements demonstrated in development of nanoparticle-based biosensors, quantum dots, plasmonic nanostructures and nanoelectronic devices that are capable of precisely evaluating concentration of pathology-specific misfolded protein/s, associated neurotransmitter fluctuations, oxidative stress mediated biomarkers, including presence of extracellular vesicle signatures in biofluids and neural tissues. Moreover, incorporation of such nanodiagnostic platforms with microfluidics and artificial intelligence-based models has further augmented the diagnostic precision efficiency and data interpretability. Although, these recent nanotechnological innovations holds the capacity to early and more accurate identification of a diseased condition, but challenges still exist with respect to their reduced biocompatibility, limited reproducibility, restricted blood-brain barrier permeability and successful translation into clinical outcomes. It is suggested that by addressing these limitations via considering the involvement of multidisciplinary approaches could usher in a new era of personalized nano-neuromedicine. Thus, by deciphering the concerted interaction between smart nanosystems and neural pathology, nanodiagnostic platforms holds the potential to transform management of neurodegenerative disease/s, from reactive treatment strategies to their proactive prevention methods.
Cancer remains one of the leading causes of mortality worldwide and poses a significant global health burden. The heterogeneity, progression, metastasis, and drug resistance qualities of cancer continue to challenge effective treatment strategies. Consequently, advanced and integrated therapeutic approaches are required to improve clinical outcomes. At present, the solutions involve combination therapy with multimodal strategies that merge the standard traditional treatment methods of surgery, chemotherapy, radiation therapy, and immunotherapy to achieve precision, prevention, and preferred outcomes. In this context, nanotechnology has emerged as a promising platform that bridges and enhances these treatment modalities. Nanotechnology-based systems, particularly in drug delivery, diagnostics, and imaging, offer improved targeting, reduced side effects, and better therapeutic efficacies. Over time, nanotechnology has evolved into an interdisciplinary field integrating material sciences, biomedical engineering, and regulatory innovations. It plays a significant role in the cancer therapeutics market, with the drug delivery systems dominating the applications, followed by biosensors, imaging agents, and tissue engineering technologies. However, challenges such as high production costs, potential long-term health risks, and environmental concerns continue to hinder its widespread clinical translation. Therefore, our critical review provides comprehensive insights and comparisons between nanotechnology-based approaches and conventional cancer therapies by highlighting their potential as multimodal treatment strategies, their roles in the therapeutic market, and the challenges associated with their implementation.
Quantum dots (QDs), a class of versatile semiconductor nanomaterials, have emerged as revolutionary tools in biomedical research due to their unique optical properties, tunable surface chemistry, and biocompatibility. This review provides a systematic overview of the fundamental characteristics of QDs, encompassing their diverse types, quantum confinement effects, photostability, synthesis strategies, and advanced characterization techniques. We discuss the cytotoxicity mechanisms of QDs and highlight surface functionalization strategies for enhancing the biocompatibility and targeting efficiency. Through precise functionalization and surface engineering, QDs have successfully been tailored for a wide array of biomedical applications, including cellular imaging, drug delivery, and single-virus tracking. However, their potential biosafety remains a paramount concern, as toxicity profiles are highly dependent on the chemical composition, particle size, and surface modifications. A key focus of this review is on recent breakthroughs in QDs-based single-virus tracking, which provides a robust framework for optimizing QDs platforms in virology research and therapeutic development. We also address the major challenges in clinical translation, such as insufficient targeting accuracy, protein corona formation, immune recognition, and scalable manufacturing. Finally, we discuss the biosafety considerations and future perspectives for the clinical translation of QDs technologies, addressing key challenges including long-term fate, regulatory hurdles, and the development of heavy-metal-free alternatives.
RNA-based therapeutics have emerged as promising strategies for treating infectious diseases, cancer, and genetic disorders owing to their ability to regulate gene expression with high specificity. However, their clinical translation remains limited by poor physiological stability, rapid nuclease degradation, inefficient cellular uptake, endosomal entrapment, and unintended immune activation. Nanomaterial-based delivery systems have therefore become essential for protecting RNA cargo, improving intracellular transport, and enabling controlled cytosolic release. This review critically examines recent advances in nanomaterial-enabled RNA delivery platforms, including lipid nanoparticles, polymeric carriers, inorganic nanomaterials, and hybrid biomimetic systems, with emphasis on how nanocarrier physicochemical properties influence RNA loading, biodistribution, cellular internalisation, and endosomal escape. Unlike conventional reviews that separately discuss RNA modalities or delivery systems, this review integrates nanocarrier design, biological barriers, intracellular trafficking, and translational feasibility within a unified design-to-clinic framework, while comparatively analysing why lipid nanoparticles have achieved clinical success whereas many alternative platforms remain translationally limited. Major biological barriers and engineering strategies, including surface functionalization, ligand-mediated targeting, and stimuli-responsive architectures, are systematically correlated with therapeutic outcomes. The review further highlights key translational design principles underlying clinically successful RNA nanotherapeutics, including balancing systemic stability, efficient endosomal escape, biocompatibility, targeted biodistribution, and scalable manufacturing. Representative clinically approved and late-stage systems are discussed alongside current translational limitations, safety concerns, and regulatory challenges. Finally, emerging directions involving artificial intelligence-guided nanocarrier engineering, biomimetic delivery systems, and multifunctional co-delivery platforms are outlined to support the future development of clinically viable RNA therapeutics.
The Extracellular Matrix (ECM) is an active component of the tumour microenvironment, which promotes tumour progression, metastasis, immune evasion, and therapeutic resistance. As a result of the pathological remodelling of the ECM in malignancy, the tissue has a novel tumour-specific signature, marked by altered composition, stiffness, and architecture that distinguishes it from normal tissue, where the ECM serves as a key, exploitable biomarker for cancer detection and intervention. Even though the importance of the ECM in the pathophysiology of cancer is well-established, the structural complexity of this biomarker has not been fully exploited in therapy. The recent advances in nanobiotechnology have provided unparalleled prospects of ECM-targeted oncologic interventions. This review critically evaluates pathological ECM remodelling in malignancy and its role in driving tumorigenesis. It examines state-of-the-art nanoimaging modalities for ECM biomarker detection, including quantum dots, gold nanoparticles, carbon nanotubes, and graphene-based biosensors. Furthermore, it details innovative therapeutic strategies such as ECM-specific drug delivery systems, enzyme-responsive nanocarriers, and nano-inhibitors targeting matrix metalloproteinases (MMPs) and lysyl oxidases (LOX). We also discuss nanotechnological approaches to modulate the ECM for enhancing immune cell infiltration and highlight the synergistic potential of combining ECM-targeted nanotherapeutics with immunotherapy and chemotherapy. Ultimately, this review highlights how integrating nanobiotechnology with matrix biology can revolutionize cancer diagnostics, theranostics, and therapy. By enabling highly specific delivery to the tumour stroma and tackling salient challenges of ECM heterogeneity, biosafety, and clinical translation, this paradigm shift from tumor-centric to matrix-centric interventions holds promise for the future of precision oncology.
Cell adhesion to the extracellular matrix (ECM) is fundamental in both cancer progression and aging. Many ECM proteins present on their surface several biofunctional adhesion cues, made of short peptide sequences that are recognized by adhesion molecules on the cell membrane. Incorporating these biofunctional peptides into hydrogels is a common strategy to promote cell adhesion to synthetic biomaterials, both for 2D and 3D cell cultures. Here, we combine atomic force microscopy (AFM) with ultrashort biofunctional peptide hydrogels to investigate the mechanobiology of single-cell adhesion in real time across three settings: young vs. senescent human dermal fibroblasts, human mesenchymal stromal cells (MSCs) from healthy donors and leukemia patients, and human neuroblastoma cells. When AFM probes were coated with self-assembling peptide hydrogels presenting cell adhesion motifs, AFM force spectroscopy profiles of the cells to biofunctional motif coated probes yield discriminating results. To our knowledge, this is the first application of ultrashort biofunctional peptide hydrogels coated on AFM tips enabling mechanobiological analysis. This work showcases a single-cell adhesion profiling tool using AFM in combination with ultrashort peptide hydrogels that simulate distinct features of the extra-cellular matrix. Our method not only provides a simple, controlled route to adhesion behavior studies and informed hydrogel sequence design decisions in label-free, physiological environment, but could also discriminate adhesion propensity of healthy, aging, and cancerous cell states, holding promise for bioengineering and regenerative medicine applications. By extending this concept, the AFM-peptide hydrogel approach offers a novel, label-free way to distinguish senescent cells and cancer-altered cells from their healthy counterparts, with potential in diagnostics and personalized therapeutics.
