Unraveling the Complex Interactions: Machine Learning Approaches to Predict Bacterial Survival against ZnO and Lanthanum-Doped ZnO Nanoparticles.

The rise in antibiotic-resistant bacteria is a global health challenge. Due to their unique properties, metal oxide nanoparticles show promise in addressing this issue. However, optimizing these properties requires a deep understanding of complex interactions. This study incorporated data-driven machine learning to predict bacterial survival against lanthanum-doped ZnO nanoparticles. The effect of incorporation of lanthanum ions on ZnO was analyzed. Even with high lanthanum concentration, no significant variations in structural, morphological, and optical properties were observed. The antibacterial activity of La-doped ZnO nanoparticles against Gram-positive and Gram-negative bacteria was qualitatively and quantitatively evaluated. Nanoparticles induce 60%, 95%, and 55% bacterial death against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, respectively. Algorithms such as Multilayer Perceptron, K-Nearest Neighbors, Gradient Boosting, and Extremely Random Trees were used to predict the bacterial survival percentage. Extremely Random Trees performed the best among these models with 95.08% accuracy. A feature relevance analysis extracted the most significant attributes to predict the bacterial survival percentage. Lanthanum content and particle size were irrelevant, despite what can be assumed. This approach offers a promising avenue for developing effective and tailored strategies to reduce the time and cost of developing antimicrobial nanoparticles.

High-performance one-dimensional halide perovskite crossbar memristors and synapses for neuromorphic computing.

Despite impressive demonstrations of memristive behavior with halide perovskites, no clear pathway for material and device design exists for their applications in neuromorphic computing. Present approaches are limited to single element structures, fall behind in terms of switching reliability and scalability, and fail to map out the analog programming window of such devices. Here, we systematically design and evaluate robust pyridinium-templated one-dimensional halide perovskites as crossbar memristive materials for artificial neural networks. We compare two halide perovskite 1D inorganic lattices, namely (propyl)pyridinium and (benzyl)pyridinium lead iodide. The absence of conjugated, electron-rich substituents in PrPyr+ prevents edge-to-face type π-stacking, leading to enhanced electronic isolation of the 1D iodoplumbate chains in (PrPyr)[PbI3], and hence, superior resistive switching performance compared to (BnzPyr)[PbI3]. We report outstanding resistive switching behaviours in (PrPyr)[PbI3] on the largest flexible crossbar implementation (16 × 16) to date - on/off ratio (>105), long term retention (105 s) and high endurance (2000 cycles). Finally, we put forth a universal approach to comprehensively map the analog programming window of halide perovskite memristive devices - a critical prerequisite for weighted synaptic connections in artificial neural networks. This consequently facilitates the demonstration of accurate handwritten digit recognition from the MNIST database based on spike-timing-dependent plasticity of halide perovskite memristive synapses.

Enhancing antioxidant properties of CeO2 nanoparticles with Nd3+ doping: structural, biological, and machine learning insights.

The antioxidant capabilities of nanoparticles are contingent upon various factors, including their shape, size, and chemical composition. Herein, novel Nd-doped CeO2 nanoparticles were synthesized and the neodymium content was varied to investigate the synergistic impact on the antioxidant properties of CeO2 nanoparticles. Incorporating Nd3+ induced changes in lattice parameters and significantly altered the morphology from nanoparticles to nanorods. The biological activity of Nd-doped CeO2 was examined against pathogenic bacterial strains, breast cancer cell lines, and antioxidant models. The antibacterial and anticancer activities of nanoparticles were not observed, which could be associated with the Ce3+/Ce4+ ratio. Notably, the incorporation of neodymium improved the antioxidant capacity of CeO2. Machine learning techniques were employed to forecast the antioxidant activity to enhance understanding and predictive capabilities. Among these models, the random forest model exhibited the highest accuracy at 96.35%, establishing it as a robust computational tool for elucidating the biological behavior of Nd-doped CeO2 nanoparticles. This study presents the first exploration of the influence of Nd3+ on the structural, optical, and biological attributes of CeO2, contributing valuable insights and extending the application of machine learning in predicting the therapeutic efficacy of inorganic nanomaterials.

Lanthanide-Doped ZnO Nanoparticles: Unraveling Their Role in Cytotoxicity, Antioxidant Capacity, and Nanotoxicology.

This study used a sonochemical synthesis method to prepare (La, Sm)-doped ZnO nanoparticles (NPs). The effect of incorporating these lanthanide elements on the structural, optical, and morphological properties of ZnO-NPs was analyzed. The cytotoxicity and the reactive oxygen species (ROS) generation capacity of ZnO-NPs were evaluated against breast (MCF7) and colon (HT29) cancer cell lines. Their antioxidant activity was analyzed using a DPPH assay, and their toxicity towards Artemia salina nauplii was also evaluated. The results revealed that treatment with NPs resulted in the death of 10.559-42.546% and 18.230-38.643% of MCF7 and HT29 cells, respectively. This effect was attributed to the ability of NPs to downregulate ROS formation within the two cell lines in a dose-dependent manner. In the DPPH assay, treatment with (La, Sm)-doped ZnO-NPs inhibited the generation of free radicals at IC50 values ranging from 3.898 to 126.948 µg/mL. Against A. salina nauplii, the synthesized NPs did not cause death nor induce morphological changes at the tested concentrations. A series of machine learning (ML) models were used to predict the biological performance of (La, Sm)-doped ZnO-NPs. Among the designed ML models, the gradient boosting model resulted in the greatest mean absolute error (MAE) (MAE 9.027, R2 = 0.86). The data generated in this work provide innovative insights into the influence of La and Sm on the structural arrangement and chemical features of ZnO-NPs, together with their cytotoxicity, antioxidant activity, and in vivo toxicity.

Closely Packed Stretchable Ultrasound Array Fabricated with Surface Charge Engineering for Contactless Gesture and Materials Detection.

Communication with hand gestures plays a significant role in human-computer interaction by providing an intuitive and natural way for humans to communicate with machines. Ultrasound-based devices have shown promising results in contactless hand gesture recognition without requiring physical contact. However, it is challenging to fabricate a densely packed wearable ultrasound array. Here, a stretchable ultrasound array is demonstrated with closely packed transducer elements fabricated using surface charge engineering between pre-charged 1-3 Lead Zirconate Titanate (PZT) composite and thin polyimide film without using a microscope. The array exhibits excellent ultrasound properties with a wide bandwidth (≈57.1%) and high electromechanical coefficient (≈0.75). The ultrasound array can decipher gestures up to 10 cm in distance by using a contactless triboelectric module and identify materials from the time constant of the exponentially decaying impedance based on their triboelectric properties by utilizing the electrostatic induction phase. The newly proposed metric of the areal-time constant is material-specific and decreases monotonically from a highly positive human body (1.13 m2 s) to negatively charged polydimethylsiloxane (PDMS) (0.02 m2 s) in the triboelectric series. The capability of the closely packed ultrasound array to detect material along with hand gesture interpretation provides an additional dimension in the next-generation human-robot interaction.

Nanocatalytic performance of pectinase immobilized over in situ prepared magnetic nanoparticles.

Immobilization of enzymes is one of the protein engineering methods used to improve their thermal and long-term stabilities. Immobilized pectinase has become an essential biocatalyst for optimization in the food processing industry. Herein, nanostructured magnetic nanoparticles were prepared in situ for use as supports to immobilize pectinase. The structural, morphological, optical and magnetic features and the chemical compositions of the nanoparticles were characterized. Nanoparticle agglomeration and low porosity were observed due to the synthetic conditions. These nanoparticles exhibited superparamagnetic behavior, which is desirable for biotechnological applications. The maximum retention rate for the enzyme was observed at pH 4.5 with a value of 1179.3 U/mgNP (units per milligram of nanoparticle), which was equivalent to a 65.6% efficiency. The free and immobilized pectinase were affected by the pH and temperature. The long-term instability caused 40% and 32% decreases in the specific activities of the free and immobilized pectinase, respectively. The effects of immobilization were analyzed with kinetic and thermodynamic studies. These results indicated a significant affinity for the substrate, a decreased reaction rate, and improved thermal stability of the immobilized pectinase. The reusability of the immobilized pectinase was preserved effectively during cycling, with only a 21.2% decrease in activity observed from the first to the last use. Therefore, alternative magnetic nanoparticles are presented for immobilizing and maintaining the thermostability of pectinase.

Iontronic pressure sensor with high sensitivity over ultra-broad linear range enabled by laser-induced gradient micro-pyramids.

Despite the extensive developments of flexible capacitive pressure sensors, it is still elusive to simultaneously achieve excellent linearity over a broad pressure range, high sensitivity, and ultrahigh pressure resolution under large pressure preloads. Here, we present a programmable fabrication method for microstructures to integrate an ultrathin ionic layer. The resulting optimized sensor exhibits a sensitivity of 33.7 kPa-1 over a linear range of 1700 kPa, a detection limit of 0.36 Pa, and a pressure resolution of 0.00725% under the pressure of 2000 kPa. Taken together with rapid response/recovery and excellent repeatability, the sensor is applied to subtle pulse detection, interactive robotic hand, and ultrahigh-resolution smart weight scale/chair. The proposed fabrication approaches and design toolkit from this work can also be leveraged to easily tune the pressure sensor performance for varying target applications and open up opportunities to create other iontronic sensors.

Deforming active droplets in viscoelastic solutions.

The motion of biological swimmers in typical bodily fluids is modelled using a system of micellar solubilization driven active droplets in a viscoelastic polymeric solution. The viscoelastic nature of the medium, as perceived by the moving droplet, characterized by the Deborah number (De), is tuned by varying the surfactant (fuel) and polymer concentration in the ambient medium. At moderate De, the droplet exhibits a steady deformed shape, markedly different from the spherical shape observed in Newtonian media. A theoretical analysis based on the normal stress balance at the interface is shown to accurately predict the droplet shape. With a further increase in De, time-periodic deformation accompanied by an oscillatory transition in swimming mode is observed. The study unveils the hitherto unexplored rich complexity in the motion of active droplets in viscoelastic fluids.

Triboelectric Nanosensor Integrated with Robotic Platform for Self-Powered Detection of Chemical Analytes.

Rapid on-site detection of hazardous chemicals is imperative for remote security and environmental monitoring applications. However, the implementation of current sensing technologies in real environments is limited due to an external high-power requirement, poor selectivity and sensitivity. Recent progress in triboelectric nanosensors and nanogenerators presents tremendous opportunities to address these issues. Here, we report an innovative self-powered triboelectric nanosensor for detection of Hg2+ ions, a harmful chemical pollutant, in a rapid single step on-site detection mechanism. Based on the mechanism of solid-liquid contact electrification, tellurium nanowire (Te NW) arrays serving as a solid triboelectric material as well as the sensing probe underwent periodic contact and separation with the Hg2+ solution, leading to the in situ formation of mercury telluride nanowire (HgTe NWs) owing to the selective binding affinity of Te NWs toward Hg2+ ions. To realize the on-site sensing potential, Te NW arrays were mounted onto the robotic hands equipped with additional wireless transmission functionality for rapid detection of Hg2+ ions in resource-limited settings by employing a simple "touch and sense" mechanism. Such a demonstration of direct integration of self-powered sensors with robotics would lead to the development of low-cost, automated chemical sensing machinery for the on-field detection of harmful analytes.

Discrimination of enantiomers and constitutional isomers by self-generated macroscopic fluid flow.

The amplification of weak molecular signals to visible output could provide a gateway to the macroscopic world. In this context, supramolecular interfaces were designed by depositing macrocyclic "host" molecules in a multilayer film that can be utilized to discriminate isomers by their fluid flow response upon "host-guest" molecular recognition.

Polymer multilayer films regulate macroscopic fluid flow and power microfluidic devices via supramolecular interactions.

Self-powered supramolecular micropumps could potentially provide a solution for powerless microfluidic devices where the fluid flow can be manipulated via modulating non-covalent interactions. An attempt has been made to fabricate thin-film-based micropumps by depositing a ß-cyclodextrin ('host') functionalized polymer on a glass slide via layer-by-layer assembly. These supramolecular micropumps turned on the fluid flow upon addition of 'guest' molecules to the multilayer films. The flow velocity was tuned using the concentration of the guest molecules as well as the number of host layers inside the multilayer films. Numerical modelling reveals that the solutal buoyancy, which originates from host-guest complexation, is primarily responsible for the fluid flow. In view of its potential application in self-powered devices, the thin-film-based micropump was integrated into a microfluidic device to show molecular and colloidal transport over long distances.

Multimodal Sensors with Decoupled Sensing Mechanisms.

Highly sensitive and multimodal sensors have recently emerged for a wide range of applications, including epidermal electronics, robotics, health-monitoring devices and human-machine interfaces. However, cross-sensitivity prevents accurate measurements of the target input signals when a multiple of them are simultaneously present. Therefore, the selection of the multifunctional materials and the design of the sensor structures play a significant role in multimodal sensors with decoupled sensing mechanisms. Hence, this review article introduces varying methods to decouple different input signals for realizing truly multimodal sensors. Early efforts explore different outputs to distinguish the corresponding input signals applied to the sensor in sequence. Next, this study discusses the methods for the suppression of the interference, signal correction, and various decoupling strategies based on different outputs to simultaneously detect multiple inputs. The recent insights into the materials' properties, structure effects, and sensing mechanisms in recognition of different input signals are highlighted. The presence of the various decoupling methods also helps avoid the use of complicated signal processing steps and allows multimodal sensors with high accuracy for applications in bioelectronics, robotics, and human-machine interfaces. Finally, current challenges and potential opportunities are discussed in order to motivate future technological breakthroughs.

Direct Laser Writing of Microscale Metal Oxide Gas Sensors from Liquid Precursors.

Fabrication and processing approaches that facilitate the ease of patterning and the integration of nanomaterials into sensor platforms are of significant utility and interest. In this work, we report the use of laser-induced thermal voxels (LITV) to fabricate microscale, planar gas sensors directly from solutions of metal salts. LITV offers a facile platform to directly integrate nanocrystalline metal oxide and mixed metal oxide materials onto heating platforms, with access to a wide variety of compositions and morphologies including many transition metals and noble metals. The unique patterning and synthesis flexibility of LITV enable the fabrication of chemically and spatially tailorable microscale sensing devices. We investigate the sensing performance of a representative set of n-type and p-type LITV-deposited metal oxides and their mixtures (CuO, NiO, CuO/ZnO, and Fe2O3/Pt) in response to reducing and oxidizing gases (H2S, NO2, NH3, ethanol, and acetone). These materials show a broad range of sensitivities and notably a strong response of NiO to ethanol and acetone (407 and 301% R/R0 at 250 °C, respectively), along with a 5- to 20-fold sensitivity enhancement for CuO/ZnO to all gases measured over pure CuO, highlighting the opportunities of LITV for the creation of mixed-material microscale sensors.

Wearable Pressure Sensors Based on MXene/Tissue Papers for Wireless Human Health Monitoring.

Though the widely available, low-cost, and disposable papers have been explored in flexible paper-based pressure sensors, it is still difficult for them to simultaneously achieve ultrahigh sensitivity, low limit and broad range of detection, and high-pressure resolution. Herein, we demonstrate a novel flexible paper-based pressure sensing platform that features the MXene-coated tissue paper (MTP) sandwiched between a polyimide encapsulation layer and a printing paper with interdigital electrodes. After replacing the polyimide with weighing paper in the MTP pressure sensor, the silver interdigital electrodes can be recycled through incineration. The resulting pressure sensor with polyimide or paper encapsulation exhibits a high sensitivity of 509.5 or 344.0 kPa-1, a low limit (∼1 Pa) and a broad range (100 kPa) of detection, and outstanding stability over 10 000 loading/unloading cycles. With ultrahigh sensitivity over a wide pressure range, the flexible pressure sensor can monitor various physiological signals and human movements. Configuring the pressure sensors into an array layout results in a smart artificial electronic skin to recognize the spatial pressure distribution. The flexible pressure sensor can also be integrated with signal processing and wireless communication modules on a face mask as a remote respiration monitoring system to wirelessly detect various respiration conditions and respiratory abnormalities for early self-identification of opioid overdose, pulmonary fibrosis, and other cardiopulmonary diseases.

Role of suction pressure in the stability of a gravity-driven thermoviscous liquid film flow down the interior surface of a cylinder.

This study aims to analyze the stability of a gravity-driven thin film flow in the heated/cooled interior surface of a vertical hollow cylinder. The model development involves simplifying the flow and energy equations using the usual thin-film approximation, where the average film thickness is considered to be much smaller than the radius of cylinder. A dispersion relation is then derived to study the temporal stability of the system in order to quantify the effect of various non-dimensional parameters present in the model, such as the thermoviscous number, Marangoni number, Biot number, and Bond number. Another non-dimensional parameter is introduced by considering an opposing suction pressure in the annulus region. The thermocapillary stress and the thermoviscous effect are shown to strongly affect the temporal stability of the flow. It is shown that although the suction pressure affects the velocity profile of the flow, it does not affect the temporal stability results. The suction pressure is then shown to have some effect on the spatiotemporal stability. Critical condition is presented for the transition between absolutely and convectively unstable systems, and parameter regimes are presented to quantify the effect of the above-mentioned parameters.

Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics.

Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decision-making, requiring the transfer of large amounts of data from periphery to central processors, at the cost of wiring, latency, fault tolerance and robustness. We envision a decentralized approach where intelligence is embedded in the sensing nodes, using a unique neuromorphic methodology to extract relevant information in robotic skins. Here we specifically address pain perception and the association of nociception with tactile perception to trigger the escape reflex in a sensorized robotic arm. The proposed system comprises self-healable materials and memtransistors as enabling technologies for the implementation of neuromorphic nociceptors, spiking local associative learning and communication. Configuring memtransistors as gated-threshold and -memristive switches, the demonstrated system features in-memory edge computing with minimal hardware circuitry and wiring, and enhanced fault tolerance and robustness.

High-k, Ultrastretchable Self-Enclosed Ionic Liquid-Elastomer Composites for Soft Robotics and Flexible Electronics.

Soft robotics focuses on mimicking natural systems to produce dexterous motion. Dielectric elastomer actuators (DEAs) are an attractive option due to their large strains, high efficiencies, lightweight design, and integrability, but require high electric fields. Conventional approaches to improve DEA performance by incorporating solid fillers in the polymer matrices can increase the dielectric constant but to the detriment of mechanical properties. In the present work, we draw inspiration from soft and deformable human skin, enabled by its unique structure, which consists of a fluid-filled membrane, to create self-enclosed liquid filler (SELF)-polymer composites by mixing an ionic liquid into the elastomeric matrix. Unlike hydrogels and ionogels, the SELF-polymer composites are made from immiscible liquid fillers, selected based on interfacial interaction with the elastomer matrix, and exist as dispersed globular phases. This combination of structure and filler selection unlocks synergetic improvements in electromechanical properties-doubling of dielectric constant, 100 times decrease in Young's modulus, and ∼5 times increase in stretchability. These composites show superior thermal stability to volatile losses, combined with excellent transparency. These ultrasoft high-k composites enable a significant improvement in the actuation performance of DEAs-longitudinal strain (5 times) and areal strain (8 times)-at low applied nominal electric fields (4 V/µm). They also enable high-sensitivity capacitive pressure sensors without the need of miniaturization and microstructuring. This class of self-enclosed ionic liquid polymer composites could impact the areas of soft robotics, shape morphing, flexible electronics, and optoelectronics.

Stability analysis of a gravity-driven thermoviscous liquid film flowing over a heated flat substrate.

Two-dimensional steady-state solutions and their stability analysis are presented for a gravity-driven thin film of a thermoviscous liquid. The governing equations and boundary conditions are simplified using the lubrication approximation. The analytically obtained film thickness evolution equation consists of various dimensionless parameters such as the Marangoni number, Biot number and thermoviscosity number. The viscosity of the liquid is assumed as an exponential function of temperature. The viscosity decreases within the liquid film as the temperature increases. Due to localized heating interfacial temperature gradients generate surface tension gradient which results into thermocapillary or Marangoni stress. The Marangoni stress opposes the fluid flow at the leading edge of heater leading to an increase in the film thickness locally. This locally thick structure becomes unstable beyond critical values of the parameters that leads to formation of rivulets in the transverse direction. Using the linear stability analysis it is found that the Marangoni stress and the thermoviscous effect have a destabilizing effect on the thin-film flow. At much higher values of the thermoviscosity number another mode of instability appears which is known as thermocapillary instability which leads to oscillating film profiles. For streamwise perturbations, the destabilizing effect of the thermoviscosity number for localized and uniform heating remains consistent.

Ultralow Power Dual-Gated Subthreshold Oxide Neuristors: An Enabler for Higher Order Neuronal Temporal Correlations.

Inspired by neural computing, the pursuit of ultralow power neuromorphic architectures with highly distributed memory and parallel processing capability has recently gained more traction. However, emulation of biological signal processing via artificial neuromorphic architectures does not exploit the immense interplay between local activities and global neuromodulations observed in biological neural networks and hence are unable to mimic complex biologically plausible adaptive functions like heterosynaptic plasticity and homeostasis. Here, we demonstrate emulation of complex neuronal behaviors like heterosynaptic plasticity, homeostasis, association, correlation, and coincidence in a single neuristor via a dual-gated architecture. This multiple gating approach allows one gate to capture the effect of local activity correlations and the second gate to represent global neuromodulations, allowing additional modulations which augment their plasticity, enabling higher order temporal correlations at a unitary level. Moreover, the dual-gate operation extends the available dynamic range of synaptic conductance while maintaining symmetry in the weight-update operation, expanding the number of accessible memory states. Finally, operating neuristors in the subthreshold regime enable synaptic weight changes with high gain while maintaining ultralow power consumption of the order of femto-Joules.