Artificial intelligence in nanotechnology

Recent developments of artificial intelligence in MXene-based devices: from synthesis to applications

Two-dimensional transition metal carbides, nitrides, or carbonitrides (MXenes) have garnered remarkable attention in various energy and environmental applications due to their high electrical conductivity, good thermal properties, large surface area, high mechanical strength, rapid charge transport mechanism, and tunable surface properties. Recently, artificial intelligence has been considered an emerging technology, and has been widely used in materials science, engineering, and biomedical applications due to its high efficiency and precision. In this review, we focus on the role of artificial intelligence-based technology in MXene-based devices and discuss the latest research directions of artificial intelligence in MXene-based devices, especially the use of artificial intelligence-based modeling tools for energy storage devices, sensors, and memristors. In addition, emphasis is given to recent progress made in synthesis methods for various MXenes and their advantages and disadvantages. Finally, the review ends with several recommendations and suggestions regarding the role of artificial intelligence in fabricating MXene-based devices. We anticipate that this review will provide guidelines on future research directions suitable for practical applications.

An Optimal Design Method for Lightweight Heating Film of Anisotropic Heat Conduction Substrate Based on Surrogate Model

In space missions, heating films are crucial for uniformly heating onboard equipment for precise temperature control. This study develops an optimization method using surrogate models for lightweight anisotropic substrate thermal conductive heating films, meeting the requirements of uniform heating in thermal control for space applications. A feedforward neural network optimized by particle swarm optimization (PSO) was employed to create a surrogate model, mapping design parameters to the temperature uniformity of the heating film. This model served as the basis for applying the NSGA-II algorithm to quickly optimize both temperature uniformity and lightweight characteristics. In this study, the PSO-BP surrogate model was trained using heating film thermal simulation data, and the surrogate model demonstrated an accurate prediction of the mean square error (MSE) of the predicted temperature difference within 0.0168 s. The maximum temperature difference in the optimal model is 1.188 ℃, which is 30.5 times lower than before optimization, and the equivalent density is only increased by 3.9%. In summary, this optimization design method effectively captures the relationships among various parameters and optimization objectives. Its superior computational accuracy and design efficiency offer significant advantages in the design of devices such as heating films.

Nanotechnology's Applications and Potential in Various Fields

Since ancient times, several sorts of nanoparticles have been employed in the quickly expanding field of nanotechnology. These features include size, shape, and chemical as well as physical properties. Because of their small size and huge surface area, carbon-based nanoparticles, including fullerenes, carbon nanotubes, graphene, graphene oxide, and carbon-based quantum dots, have attracted a lot of attention in a variety of sectors, including biomedical applications. Lipid bilayers form the spherical vesicles known as liposomes. Magnetic resonance imaging (MRI) contrast agents are iron oxide nanoparticles. These materials are perfect for drug and delivery of genes, bioimaging, and bone repair because of their remarkable mechanical, electrical, visual, and chemical properties. However, concerns about potential asbestos-related diseases have arisen due to their length-to-width aspect ratio. Ceramic nanoparticles, on the other hand, are a common material in daily life and play a crucial role in bone repair, multiscale hybridisation, and aerospace structures. These nanoparticles can enhance osseointegration and bone development by mimicking the nanocomposition and nanoscale characteristics of bone tissue and enhance osteoconductive and osteoinductive capacities. Ceramic nanoparticles, however, have the potential to generate oxidative stress, which can result in irritation of the reticuloendothelial system, cytotoxicity to the heart, liver, and lungs, as well as toxicity to the cells that are attached. Additionally, oxidative stress, cell damage, and genotoxicity might result from the generation of free radicals by ceramic nanoparticles. Metal nanoparticles exhibit linear optical properties similar to molecular systems but arise from a different physical process. Semiconductor nanocrystals (NCs) are made from various compounds, such as silicon and germanium. Polyandry nanoparticles are particles approximately 10 and 10000 nanometers (nm) in size that can contain active substances. They have applications in vaccine delivery, gene therapy, and polymer nanoparticles (nanomedicine) for therapeutic applications.

Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence

Cell-based drug delivery systems are new strategies in targeted delivery in which cells or cell-membrane-derived systems are used as carriers and release their cargo in a controlled manner. Recently, great attention has been directed to cells as carrier systems for treating several diseases. There are various challenges in the development of cell-based drug delivery systems. The prediction of the properties of these platforms is a prerequisite step in their development to reduce undesirable effects. Integrating nanotechnology and artificial intelligence leads to more innovative technologies. Artificial intelligence quickly mines data and makes decisions more quickly and accurately. Machine learning as a subset of the broader artificial intelligence has been used in nanomedicine to design safer nanomaterials. Here, how challenges of developing cell-based drug delivery systems can be solved with potential predictive models of artificial intelligence and machine learning is portrayed. The most famous cell-based drug delivery systems and their challenges are described. Last but not least, artificial intelligence and most of its types used in nanomedicine are highlighted. The present Review has shown the challenges of developing cells or their derivatives as carriers and how they can be used with potential predictive models of artificial intelligence and machine learning.

Synthesis and Characterization of Polyhydroxyalkanoate/Graphene Oxide/Nanoclay Bionanocomposites: Experimental Results and Theoretical Predictions via Machine Learning Models

Predicting the mechanical properties of multiscale nanocomposites requires simulations that are costly from a practical viewpoint and time consuming. The use of algorithms for property prediction can reduce the extensive experimental work, saving time and costs. To assess this, ternary poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV)-based bionanocomposites reinforced with graphene oxide (GO) and montmorillonite nanoclay were prepared herein via an environmentally friendly electrochemical process followed by solution casting. The aim was to evaluate the effectiveness of different Machine Learning (ML) models, namely Artificial Neural Network (ANN), Decision Tree (DT), and Support Vector Machine (SVM), in predicting their mechanical properties. The algorithms' input data were the Young's modulus, tensile strength, and elongation at break for various concentrations of the nanofillers (GO and nanoclay). The correlation coefficient (R2), mean absolute error (MAE), and mean square error (MSE) were used as statistical indicators to assess the performance of the models. The results demonstrated that ANN and SVM are useful for estimating the Young's modulus and elongation at break, with MSE values in the range of 0.64-1.0% and 0.14-0.28%, respectively. On the other hand, DT was more suitable for predicting the tensile strength, with the indicated error in the range of 0.02-9.11%. This study paves the way for the application of ML models as confident tools for predicting the mechanical properties of polymeric nanocomposites reinforced with different types of nanofiller, with a view to using them in practical applications such as biomedicine.

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

Artificial Intelligence With Robotics in Healthcare: A Narrative Review of Its Viability in India

This short review focuses on the emerging role of artificial intelligence (AI) with robotics in the healthcare sector. It may have particular utility for India, which has limited access to healthcare providers for a large growing population and limited health resources in rural India. AI works with an amalgamation of enormous amounts of data using fast and complex algorithms. This permits the software to quickly adapt the pattern of the data characteristics. It has the possibility to collide with most of the facets of the health system which may range from discovery to prediction and deterrence. The use of AI with robotics in the healthcare sector has shown a remarkable rising trend in the past few years. Functions like assistance with surgery, streamlining hospital logistics, and conducting routine checkups are some of the tasks that may be managed with great efficiency using artificial intelligence in urban and rural hospitals across the country. AI in the healthcare sector is advantageous in terms of ensuring exclusive patient care, safe working conditions where healthcare providers are at a lower risk of getting infected, and perfectly organized operational tasks. As the healthcare segment is globally recognized as one of the most dynamic and biggest industries, it tends to expedite development through modernization and original approaches. The future of this lucrative industry is looking forward to a great revolution aiming to create intelligent machines that work and respond like human beings. The future perspective of AI and robotics in the healthcare sector encompasses the care of elderly people, drug discovery, diagnosis of deadly diseases, a boost in clinical trials, remote patient monitoring, prediction of epidemic outbreaks, etc. However, the viability of using robotics in healthcare may be questionable in terms of expenditure, skilled workforce, and the conventional mindset of people. The biggest challenge is the replication of these technologies to the smaller towns and rural areas so that these facilities may reach the larger segment of the entire population of the country. This review aims to examine the adaptability and viability of these new technologies in the Indian scenario and identify the major challenges.

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.

Nanoparticles and convergence of artificial intelligence for targeted drug delivery for cancer therapy: Current progress and challenges

Cancer is a life-threatening disease, resulting in nearly 10 million deaths worldwide. There are various causes of cancer, and the prognostic information varies in each patient because of unique molecular signatures in the human body. However, genetic heterogeneity occurs due to different cancer types and changes in the neoplasms, which complicates the diagnosis and treatment. Targeted drug delivery is considered a pivotal contributor to precision medicine for cancer treatments as this method helps deliver medication to patients by systematically increasing the drug concentration on the targeted body parts. In such cases, nanoparticle-mediated drug delivery and the integration of artificial intelligence (AI) can help bridge the gap and enhance localized drug delivery systems capable of biomarker sensing. Diagnostic assays using nanoparticles (NPs) enable biomarker identification by accumulating in the specific cancer sites and ensuring accurate drug delivery planning. Integrating NPs for cancer targeting and AI can help devise sophisticated systems that further classify cancer types and understand complex disease patterns. Advanced AI algorithms can also help in biomarker detection, predicting different NP interactions of the targeted drug, and evaluating drug efficacy. Considering the advantages of the convergence of NPs and AI for targeted drug delivery, there has been significantly limited research focusing on the specific research theme, with most of the research being proposed on AI and drug discovery. Thus, the study's primary objective is to highlight the recent advances in drug delivery using NPs, and their impact on personalized treatment plans for cancer patients. In addition, a focal point of the study is also to highlight how integrating AI, and NPs can help address some of the existing challenges in drug delivery by conducting a collective survey.

Modeling the optical properties of transparent and absorbing dielectrics by means of symbolic regression

In this contribution we explore the possibilities and limitations of symbolic regression as an alternative to the approaches currently used to characterize the dispersive behavior of a given material. To this end, we make use of genetic programming to retrieve, from either ellipsometric or spectral data, closed-form expressions that model the optical properties of the materials studied. In a first stage we consider transparent dielectrics for our numerical experiments. Next we increase the complexity of the problem and consider absorbing dielectrics, which not only require the use of complex functions to model their dielectric function, but also imply a supplementary constraint imposed by the verification of the causality principle.

A small data-driven predictive model for adsorption properties in polymeric thin film

Artificial intelligence allowing data-driven prediction of physicochemical properties of polymer is rapidly emerging as a powerful tool for advancing material science. Here, we provide a methodology to perceive polymer adsorption...

Integrated intelligent computing application for effectiveness of Au nanoparticles coated over MWCNTs with velocity slip in curved channel peristaltic flow

Estimation of the effectiveness of Au nanoparticles concentration in peristaltic flow through a curved channel by using a data driven stochastic numerical paradigm based on artificial neural network is presented in this study. In the modelling, nano composite is considered involving multi-walled carbon nanotubes coated with gold nanoparticles with different slip conditions. Modeled differential system of the physical problem is numerically analyzed for different scenarios to predict numerical data for velocity and temperature by Adams Bashforth method and these solutions are used as a reference dataset of the networks. Data is processed by segmentation into three categories i.e., training, validation and testing while Levenberg-Marquart training algorithm is adopted for optimization of networks results in terms of performance on mean square errors, train state plots, error histograms, regression analysis, time series responses, and auto-correlation, which establish the accurate and efficient recognition of trends of the system.

Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks

Silicon photonics is a low-cost and versatile platform for various applications. For design of silicon photonic devices, the light-material interaction within its complex subwavelength geometry is difficult to investigate analytically and therefore numerical simulations are majorly adopted. To make the design process more time-efficient and to improve the device performance to its physical limits, various methods have been proposed over the past few years to manipulate the geometries of silicon platform for specific applications. In this review paper, we summarize the design methodologies for silicon photonics including iterative optimization algorithms and deep neural networks. In case of iterative optimization methods, we discuss them in different scenarios in the sequence of increased degrees of freedom: empirical structure, QR-code like structure and irregular structure. We also review inverse design approaches assisted by deep neural networks, which generate multiple devices with similar structure much faster than iterative optimization methods and are thus suitable in situations where piles of optical components are needed. Finally, the applications of inverse design methodology in optical neural networks are also discussed. This review intends to provide the readers with the suggestion for the most suitable design methodology for a specific scenario.

Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin

The application of machine learning (ML) in bioprinting has attracted considerable attention recently. Many have focused on the benefits and potential of ML, but a clear overview of how ML shapes the future of three-dimensional (3D) bioprinting is still lacking. Here, it is proposed that two missing links, Big Data and Digital Twin, are the key to articulate the vision of future 3D bioprinting. Creating training databases from Big Data curation and building digital twins of human organs with cellular resolution and properties are the most important and urgent challenges. With these missing links, it is envisioned that future 3D bioprinting will become more digital and in silico, and eventually strike a balance between virtual and physical experiments toward the most efficient utilization of bioprinting resources. Furthermore, the virtual component of bioprinting and biofabrication, namely, digital bioprinting, will become a new growth point for digital industry and information technology in future.

Artificial intelligence in drug discovery and development

Artificial intelligence-integrated drug discovery and development has accelerated the growth of the pharmaceutical sector, leading to a revolutionary change in the pharma industry. Here, we discuss areas of integration, tools, and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them.

Multifunctional biomolecule nanostructures for cancer therapy

Biomolecule-based nanostructures are inherently multifunctional and harbour diverse biological activities, which can be explored for cancer nanomedicine. The supramolecular properties of biomolecules can be precisely programmed for the design of smart drug delivery vehicles, enabling efficient transport in vivo, targeted drug delivery and combinatorial therapy within a single design. In this Review, we discuss biomolecule-based nanostructures, including polysaccharides, nucleic acids, peptides and proteins, and highlight their enormous design space for multifunctional nanomedicines. We identify key challenges in cancer nanomedicine that can be addressed by biomolecule-based nanostructures and survey the distinct biological activities, programmability and in vivo behaviour of biomolecule-based nanostructures. Finally, we discuss challenges in the rational design, characterization and fabrication of biomolecule-based nanostructures, and identify obstacles that need to be overcome to enable clinical translation.

Nanotheranostics against COVID-19: From multivalent to immune-targeted materials

Destructive impacts of COVID-19 pandemic worldwide necessitates taking more appropriate measures for mitigating virus spread and development of the effective theranostic agents. In general, high heterogeneity of viruses is a major challenging issue towards the development of effective antiviral agents. Regarding the coronavirus, its high mutation rates can negatively affect virus detection process or the efficiency of drugs and vaccines in development or induce drug resistance. Bioengineered nanomaterials with suitable physicochemical characteristics for site-specific therapeutic delivery, highly-sensitive nanobiosensors for detection of very low virus concentration, and real-time protections using the nanorobots can provide roadmaps towards the imminent breakthroughs in theranostics of a variety of diseases including the COVID-19. Besides revolutionizing the classical disinfection procedures, state-of-the-art nanotechnology-based approaches enable providing the analytical tools for accelerated monitoring of coronavirus and associated biomarkers or drug delivery towards the pulmonary system or other affected organs. Multivalent nanomaterials capable of interaction with multivalent pathogens including the viruses could be suitable candidates for viral detection and prevention of further infections. Besides the inactivation or destruction of the virus, functionalized nanoparticles capable of modulating patient's immune response might be of great significance for attenuating the exaggerated inflammatory reactions or development of the effective nanovaccines and medications against the virus pandemics including the COVID-19.

A bird's-eye view of deep learning in bioimage analysis

Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird's-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.

Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

No characterization method is available to quickly perform quality inspection of 2D materials produced on an industrial scale. This hinders the adoption of 2D materials for product manufacturing in many industries. Here, we report an artificial-intelligence-assisted Raman analysis to quickly probe the quality of centimeter-large graphene samples in a non-destructive manner. Chemical vapor deposition of graphene is devised in this work such that two types of samples were obtained: layer-plus-islands and layer-by-layer graphene films, at centimeter scales. Using these samples, we implemented and integrated an unsupervised learning algorithm with an automated Raman spectroscopy to precisely cluster 20,250 and 18,000 Raman spectra collected from layer-plus-islands and layer-by-layer graphene films, respectively, into five and two clusters. Each cluster represents graphene patches with different layer numbers and stacking orders. For instance, the two clusters detected in layer-by-layer graphene films represent monolayer and bilayer graphene based on their Raman fingerprints. Our intelligent Raman analysis is fully automated, with no human operation involved, is highly reliable (99.95% accuracy), and can be generalized to other 2D materials, paving the way towards industrialization of 2D materials for various applications in the future.

Oliveira O, Mejía-Salazar JR. Chiral Plasmonics and Their Potential for Point-of-Care Biosensing Applications

There has been growing interest in using strong field enhancement and light localization in plasmonic nanostructures to control the polarization properties of light. Various experimental techniques are now used to fabricate twisted metallic nanoparticles and metasurfaces, where strongly enhanced chiral near-fields are used to intensify circular dichroism (CD) signals. In this review, state-of-the-art strategies to develop such chiral plasmonic nanoparticles and metasurfaces are summarized, with emphasis on the most recent trends for the design and development of functionalizable surfaces. The major objective is to perform enantiomer selection which is relevant in pharmaceutical applications and for biosensing. Enhanced sensing capabilities are key for the design and manufacture of lab-on-a-chip devices, commonly named point-of-care biosensing devices, which are promising for next-generation healthcare systems.

The significance of artificial intelligence in drug delivery system design

Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.

Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images

The fast progress in research and development of multifunctional, distributed sensor networks has brought challenges in processing data from a large number of sensors. Using deep learning methods such as convolutional neural networks (CNN), it is possible to build smarter systems to forecasting future situations as well as precisely classify large amounts of data from sensors. Multi-sensor data from atmospheric pollutants measurements that involves five criteria, with the underlying analytic model unknown, need to be categorized, so do the Diabetic Retinopathy (DR) fundus images dataset. In this work, we created automatic classifiers based on a deep convolutional neural network (CNN) with two models, a simpler feedforward model with dual modules and an Inception Resnet v2 model, and various structural tweaks for classifying the data from the two tasks. For segregating multi-sensor data, we trained a deep CNN-based classifier on an image dataset extracted from the data by a novel image generating method. We created two deepened and one reductive feedforward network for DR phase classification. The validation accuracies and visualization results show that increasing deep CNN structure depth or kernels number in convolutional layers will not indefinitely improve the classification quality and that a more sophisticated model does not necessarily achieve higher performance when training datasets are quantitatively limited, while increasing training image resolution can induce higher classification accuracies for trained CNNs. The methodology aims at providing support for devising classification networks powering intelligent sensors.

Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale

Nanophotonics has been an active research field over the past two decades, triggered by the rising interests in exploring new physics and technologies with light at the nanoscale. As the demands of performance and integration level keep increasing, the design and optimization of nanophotonic devices become computationally expensive and time-inefficient. Advanced computational methods and artificial intelligence, especially its subfield of machine learning, have led to revolutionary development in many applications, such as web searches, computer vision, and speech/image recognition. The complex models and algorithms help to exploit the enormous parameter space in a highly efficient way. In this review, we summarize the recent advances on the emerging field where nanophotonics and machine learning blend. We provide an overview of different computational methods, with the focus on deep learning, for the nanophotonic inverse design. The implementation of deep neural networks with photonic platforms is also discussed. This review aims at sketching an illustration of the nanophotonic design with machine learning and giving a perspective on the future tasks.

Metamodeling of high-contrast-index gratings for color reproduction

We propose a metamodel-based optimization technique to tailor the chromatic response of high-contrast-index gratings. The algorithm, which couples a population-based metaheuristic with a neural network, is used to retrieve the optimal geometrical parameters of a grating to reproduce a prescribed color. By means of some examples, we assess the possibilities and limitations of our optimization scheme. The numerical evidence found shows that the metamodel approach offers an alternative to traditional metaheuristic techniques that not only provides the best solution for a given geometry and a material but also significantly improves the computing time required for the optimization process.

A New Frontier: The Convergence of Nanotechnology, Brain Machine Interfaces, and Artificial Intelligence

A confluence of technological capabilities is creating an opportunity for machine learning and artificial intelligence (AI) to enable "smart" nanoengineered brain machine interfaces (BMI). This new generation of technologies will be able to communicate with the brain in ways that support contextual learning and adaptation to changing functional requirements. This applies to both invasive technologies aimed at restoring neurological function, as in the case of neural prosthesis, as well as non-invasive technologies enabled by signals such as electroencephalograph (EEG). Advances in computation, hardware, and algorithms that learn and adapt in a contextually dependent way will be able to leverage the capabilities that nanoengineering offers the design and functionality of BMI. We explore the enabling capabilities that these devices may exhibit, why they matter, and the state of the technologies necessary to build them. We also discuss a number of open technical challenges and problems that will need to be solved in order to achieve this.

Interface Characterization and Control of 2D Materials and Heterostructures

2D materials and heterostructures have attracted significant attention for a variety of nanoelectronic and optoelectronic applications. At the atomically thin limit, the material characteristics and functionalities are dominated by surface chemistry and interface coupling. Therefore, methods for comprehensively characterizing and precisely controlling surfaces and interfaces are required to realize the full technological potential of 2D materials. Here, the surface and interface properties that govern the performance of 2D materials are introduced. Then the experimental approaches that resolve surface and interface phenomena down to the atomic scale, as well as strategies that allow tuning and optimization of interfacial interactions in van der Waals heterostructures, are systematically reviewed. Finally, a future outlook that delineates the remaining challenges and opportunities for 2D material interface characterization and control is presented.

Plasmonic nanostructure design and characterization via Deep Learning

Nanophotonics, the field that merges photonics and nanotechnology, has in recent years revolutionized the field of optics by enabling the manipulation of light-matter interactions with subwavelength structures. However, despite the many advances in this field, the design, fabrication and characterization has remained widely an iterative process in which the designer guesses a structure and solves the Maxwell's equations for it. In contrast, the inverse problem, i.e., obtaining a geometry for a desired electromagnetic response, remains a challenging and time-consuming task within the boundaries of very specific assumptions. Here, we experimentally demonstrate that a novel Deep Neural Network trained with thousands of synthetic experiments is not only able to retrieve subwavelength dimensions from solely far-field measurements but is also capable of directly addressing the inverse problem. Our approach allows the rapid design and characterization of metasurface-based optical elements as well as optimal nanostructures for targeted chemicals and biomolecules, which are critical for sensing, imaging and integrated spectroscopy applications.

Feedforward neural network methodology to characterize thin films by Electrostatic Force Microscopy

The contribution of the present paper is in introducing a numerical method to improve the automatic characterization of thin films by increasing the effectiveness of numerical methods that take into account the macroscopic shape of the tip. To achieve this objective, we propose the combination of different feedforward neural networks architectures adapted to the specific requirements of the physical system under study. First, an Adaline architecture is redefined as a linear combination of Green functions obtained from the Laplace equation. The learning process is also redefined to accurately calculate the electrostatic charges inside the tip. We demonstrate that a complete training set for the characterization of thin films can be easily obtained by this methodology. The characterization of the sample is developed in a second stage where a multilayer perceptron is adapted to work efficiently in experimental conditions where some experimental data can be lost. We demonstrate that a very efficient strategy is to use evolutionary algorithms as training method. By the modulation of the fit function, we can improve the network performance in the characterization of thin films where some information is missing or altered by experimental noise due to the small tip-sample working distances. By doing so, we can discriminate the conductive properties of thin films from force curves that have been altered explicitly to simulate realistic experimental conditions.

Determination of the complex refractive index of a subwavelength-diameter platinum or gold pipe by light scattering

The complex refractive indices of Pt and Au pipes that are subwavelength in diameter have been found to be different from those of metal thin films for the first time. The metal pipe is made from a spider silk of half-wavelength diameter clad with Pt or Au and illuminated by a plane-polarized laser of wavelength 660 nm at normal incidence. The angular distribution of the light intensity scattered by the pipe is measured and fitted using theoretical calculations based on the corresponding model. The fitting results have lead to the optimum values and uncertainty ranges of the indices and the diameter of the pipe. A field emission scanning electron microscope confirms the diameter from the optical estimation and reveals an image of the surface of the pipe.

Nanostructures: a platform for brain repair and augmentation

Nanoscale structures have been at the core of research efforts dealing with integration of nanotechnology into novel electronic devices for the last decade. Because the size of nanomaterials is of the same order of magnitude as biomolecules, these materials are valuable tools for nanoscale manipulation in a broad range of neurobiological systems. For instance, the unique electrical and optical properties of nanowires, nanotubes, and nanocables with vertical orientation, assembled in nanoscale arrays, have been used in many device applications such as sensors that hold the potential to augment brain functions. However, the challenge in creating nanowires/nanotubes or nanocables array-based sensors lies in making individual electrical connections fitting both the features of the brain and of the nanostructures. This review discusses two of the most important applications of nanostructures in neuroscience. First, the current approaches to create nanowires and nanocable structures are reviewed to critically evaluate their potential for developing unique nanostructure based sensors to improve recording and device performance to reduce noise and the detrimental effect of the interface on the tissue. Second, the implementation of nanomaterials in neurobiological and medical applications will be considered from the brain augmentation perspective. Novel applications for diagnosis and treatment of brain diseases such as multiple sclerosis, meningitis, stroke, epilepsy, Alzheimer's disease, schizophrenia, and autism will be considered. Because the blood brain barrier (BBB) has a defensive mechanism in preventing nanomaterials arrival to the brain, various strategies to help them to pass through the BBB will be discussed. Finally, the implementation of nanomaterials in neurobiological applications is addressed from the brain repair/augmentation perspective. These nanostructures at the interface between nanotechnology and neuroscience will play a pivotal role not only in addressing the multitude of brain disorders but also to repair or augment brain functions.