Classification with a disordered dopant-atom network in silicon.

Classification is an important task at which both biological and artificial neural networks excel1,2. In machine learning, nonlinear projection into a high-dimensional feature space can make data linearly separable3,4, simplifying the classification of complex features. Such nonlinear projections are computationally expensive in conventional computers. A promising approach is to exploit physical materials systems that perform this nonlinear projection intrinsically, because of their high computational density5, inherent parallelism and energy efficiency6,7. However, existing approaches either rely on the systems' time dynamics, which requires sequential data processing and therefore hinders parallel computation5,6,8, or employ large materials systems that are difficult to scale up7. Here we use a parallel, nanoscale approach inspired by filters in the brain1 and artificial neural networks2 to perform nonlinear classification and feature extraction. We exploit the nonlinearity of hopping conduction9-11 through an electrically tunable network of boron dopant atoms in silicon, reconfiguring the network through artificial evolution to realize different computational functions. We first solve the canonical two-input binary classification problem, realizing all Boolean logic gates12 up to room temperature, demonstrating nonlinear classification with the nanomaterial system. We then evolve our dopant network to realize feature filters2 that can perform four-input binary classification on the Modified National Institute of Standards and Technology handwritten digit database. Implementation of our material-based filters substantially improves the classification accuracy over that of a linear classifier directly applied to the original data13. Our results establish a paradigm of silicon-based electronics for small-footprint and energy-efficient computation14.

Electrochemical Detection of Tumor-Derived Extracellular Vesicles on Nanointerdigitated Electrodes.

Tumor-derived extracellular vesicles (tdEVs) are attracting much attention due to their essential function in intercellular communication and their potential as cancer biomarkers. Although tdEVs are significantly more abundant in blood than other cancer biomarkers, their concentration compared to other blood components remains relatively low. Moreover, the presence of particles in blood with a similar size as that of tdEVs makes their selective and sensitive detection further challenging. Therefore, highly sensitive and specific biosensors are required for unambiguous tdEV detection in complex biological environments, especially for decentralized point-of-care analysis. Here, we report an electrochemical sensing scheme for tdEV detection, with two-level selectivity provided by a sandwich immunoassay and two-level amplification through the combination of an enzymatic assay and redox cycling on nanointerdigitated electrodes to respectively enhance the specificity and sensitivity of the assay. Analysis of prostate cancer cell line tdEV samples at various concentrations revealed an estimated limit of detection for our assay as low as 5 tdEVs/µL, as well as an excellent linear sensor response spreading over 6 orders of magnitude (10-106 tdEVs/µL), which importantly covers the clinically relevant range for tdEV detection in blood. This novel nanosensor and associated sensing scheme opens new opportunities to detect tdEVs at clinically relevant concentrations from a single blood finger prick.

Hard Superconducting Gap and Diffusion-Induced Superconductors in Ge-Si Nanowires.

We show a hard superconducting gap in a Ge-Si nanowire Josephson transistor up to in-plane magnetic fields of 250 mT, an important step toward creating and detecting Majorana zero modes in this system. A hard gap requires a highly homogeneous tunneling heterointerface between the superconducting contacts and the semiconducting nanowire. This is realized by annealing devices at 180 °C during which aluminum interdiffuses and replaces the germanium in a section of the nanowire. Next to Al, we find a superconductor with lower critical temperature (TC = 0.9 K) and a higher critical field (BC = 0.9-1.2 T). We can therefore selectively switch either superconductor to the normal state by tuning the temperature and the magnetic field and observe that the additional superconductor induces a proximity supercurrent in the semiconducting part of the nanowire even when the Al is in the normal state. In another device where the diffusion of Al rendered the nanowire completely metallic, a superconductor with a much higher critical temperature (TC = 2.9 K) and critical field (BC = 3.4 T) is found. The small size of these diffusion-induced superconductors inside nanowires may be of special interest for applications requiring high magnetic fields in arbitrary direction.

Haemophilia care and outcome in a major haemophilia treatment centre in Malaysia.

INTRODUCTION/OBJECTIVE: The management of potential treatment-related complications and bleeding events in haemophilia is challenging in developing countries. Providing optimal care among these patients improve their quality of life (QOL) and life expectancy. This study explores the demographic characteristics and treatment outcome in a major haemophilia treatment centre in Malaysia. MATERIALS AND METHODS: A total of 260 patients were recruited in this retrospective cross-sectional analysis. Clinical data, including treatment regimens and outcome, were collected and analysed. RESULTS: A total of 211 patients were diagnosed with haemophilia A (HA) (severe disease, 72.5%) and 49 patients had haemophilia B (HB) (severe disease, 65.3%). The median age was 31 (IQR;2-84) years. Majority of the patients had at least one episode of musculoskeletal bleeding since diagnosis. The mean annual bleeding event (ABE) was 4.91 (SD±6.07) in 2018. Target joints were identified in 80.4% of the patients. Chronic arthropathy and synovitis collectively accounted for more than half of the musculoskeletal complications. 30.1% of the patients had contracted hepatitis C with less than half received treatment. Thirty-one patients (16.8%) with severe haemophilia developed inhibitor and 12 patients successfully underwent immune tolerance induction. More than three-quarters of the severe haemophilia patients were treated with factor concentrate prophylaxis. The mean prophylaxis dose for HA and HB were 41.3 (SD±19.1) and 48.6 (SD±21.5) IU/kg/week, respectively. In patients with severe disease, prophylaxis significantly reduced the ABE (5.45,9.03;p=0.005). CONCLUSION: The importance of utilising a low to moderate dose regimen as prophylaxis in haemophilic patients is highlighted in our study. Future studies should include QOL assessment will further improve the management in haemophilia.

Electron Trapping Mechanism in LaAlO_{3}/SrTiO_{3} Heterostructures.

In LaAlO_{3}/SrTiO_{3} heterostructures, a still poorly understood phenomenon is that of electron trapping in back-gating experiments. Here, by combining magnetotransport measurements and self-consistent Schrödinger-Poisson calculations, we obtain an empirical relation between the amount of trapped electrons and the gate voltage. The amount of trapped electrons decays exponentially away from the interface. However, contrary to earlier observations, we find that the Fermi level remains well within the quantum well. The enhanced trapping of electrons induced by the gate voltage can therefore not be explained by a thermal escape mechanism. Further gate sweeping experiments strengthen that conclusion. We propose a new mechanism which involves the electromigration and clustering of oxygen vacancies in SrTiO_{3} and argue that such electron trapping is a universal phenomenon in SrTiO_{3}-based two-dimensional electron systems.

A fabrication guide for planar silicon quantum dot heterostructures.

We describe important considerations to create top-down fabricated planar quantum dots in silicon, often not discussed in detail in literature. The subtle interplay between intrinsic material properties, interfaces and fabrication processes plays a crucial role in the formation of electrostatically defined quantum dots. Processes such as oxidation, physical vapor deposition and atomic-layer deposition must be tailored in order to prevent unwanted side effects such as defects, disorder and dewetting. In two directly related manuscripts written in parallel we use techniques described in this work to create depletion-mode quantum dots in intrinsic silicon, and low-disorder silicon quantum dots defined with palladium gates. While we discuss three different planar gate structures, the general principles also apply to 0D and 1D systems, such as self-assembled islands and nanowires.

Versatile Micropatterns of N-Heterocyclic Carbenes on Gold Surfaces: Increased Thermal and Pattern Stability with Enhanced Conductivity.

Patterned monolayers of N-heterocyclic carbenes (NHCs) on gold surfaces were obtained by microcontact printing of NHC-CO2 adducts and NHC(H)[HCO3 ] salts. The NHC-modified areas showed an increased conductivity compared to unmodified gold surface areas. Furthermore, the remaining surface areas could be modified with a second, azide-functionalized carbene, facilitating further applications and post-printing modifications. Thorough elucidation by a variety of analytical methods offers comprehensive evidence for the viability of the methodology reported here. The protocol enables facile access to versatile, microstructured NHC-modified gold surfaces with highly stable patterns, enhanced conductivity, and the option for further modification.

Monolayer Contact Doping from a Silicon Oxide Source Substrate.

Monolayer contact doping (MLCD) is a modification of the monolayer doping (MLD) technique that involves monolayer formation of a dopant-containing adsorbate on a source substrate. This source substrate is subsequently brought into contact with the target substrate, upon which the dopant is driven into the target substrate by thermal annealing. Here, we report a modified MLCD process, in which we replace the commonly used Si source substrate by a thermally oxidized substrate with a 100 nm thick silicon oxide layer, functionalized with a monolayer of a dopant-containing silane. The thermal oxide potentially provides a better capping effect and effectively prevents the dopants from diffusing back into the source substrate. The use of easily accessible and processable silane monolayers provides access to a general and modifiable process for the introduction of dopants on the source substrate. As a proof of concept, a boron-rich carboranyl-alkoxysilane was used here to construct the monolayer that delivers the dopant, to boost the doping level in the target substrate. X-ray photoelectron spectroscopy (XPS) showed a successful grafting of the dopant adsorbate onto the SiO2 surface. The achieved doping levels after thermal annealing were similar to the doping levels acessible by MLD as demonstrated by secondary ion mass spectrometry measurements. The method shows good prospects, e.g. for use in the doping of Si nanostructures.

Electron-Hole Confinement Symmetry in Silicon Quantum Dots.

We report electrical transport measurements on a gate-defined ambipolar quantum dot in intrinsic silicon. The ambipolarity allows its operation as either an electron or a hole quantum dot of which we change the dot occupancy by 20 charge carriers in each regime. Electron-hole confinement symmetry is evidenced by the extracted gate capacitances and charging energies. The results demonstrate that ambipolar quantum dots offer great potential for spin-based quantum information processing, since confined electrons and holes can be compared and manipulated in the same crystalline environment.

When Machine Learning Meets 2D Materials: A Review.

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

Covalent Coupling of Nanoparticles with Low-Density Functional Ligands to Surfaces via Click Chemistry.

We demonstrate the application of the 1,3-dipolar cycloaddition ("click" reaction) to couple gold nanoparticles (Au NPs) functionalized with low densities of functional ligands. The ligand coverage on the citrate-stabilized Au NPs was adjusted by the ligand:Au surface atom ratio, while maintaining the colloidal stability of the Au NPs in aqueous solution. A procedure was developed to determine the driving forces governing the selectivity and reactivity of citrate-stabilized and ligand-functionalized Au NPs on patterned self-assembled monolayers. We observed selective and remarkably stable chemical bonding of the Au NPs to the complimentarily functionalized substrate areas, even when estimating that only 1-2 chemical bonds are formed between the particles and the substrate.

Toward a formal theory for computing machines made out of whatever physics offers.

Approaching limitations of digital computing technologies have spurred research in neuromorphic and other unconventional approaches to computing. Here we argue that if we want to engineer unconventional computing systems in a systematic way, we need guidance from a formal theory that is different from the classical symbolic-algorithmic Turing machine theory. We propose a general strategy for developing such a theory, and within that general view, a specific approach that we call fluent computing. In contrast to Turing, who modeled computing processes from a top-down perspective as symbolic reasoning, we adopt the scientific paradigm of physics and model physical computing systems bottom-up by formalizing what can ultimately be measured in a physical computing system. This leads to an understanding of computing as the structuring of processes, while classical models of computing systems describe the processing of structures.

Magnetic nanoparticle assembly on surfaces using click chemistry.

Controlled assembly of ferromagnetic nanoparticles on surfaces is of crucial importance for a range of spintronic and data storage applications. Here, we present a novel method for assembling monolayers of ferromagnetic FePt nanoparticles on silicon oxide substrates using "click chemistry". Reaction of alkyne-functionalized FePt nanoparticles with azide-terminated self-assembled monolayers (SAMs), on silicon oxide, leads to the irreversible attachment of magnetic nanoparticles to the surface via triazole linkers. Based on this covalent interaction, well-packed monolayers of FePt nanoparticles were prepared and nanoparticle patterns are generated on surfaces via microcontact printing (µCP).

Does antiretroviral therapy improve HIV-associated cognitive impairment? A quantitative review of the literature.

The development of antiretroviral therapy (ART) has dramatically improved survival for those living with human immunodeficiency virus (HIV), but whether ART improves cognitive functioning remains unclear. The aim of the present review was to examine systematically the extent to which ART improves cognition among individuals with HIV using meta-analytic methods. Twenty-three studies were included in the quantitative review. ART was associated with modest improvements in attention (mean d = .17; p < .001; 95% confidence interval [CI], .09/.25), executive function (mean d = .18; p < .001; 95% CI, .10/.26), and motor function (mean d = .24; p < .001; 95% CI, .16/.32). ART did not improve language, verbal memory, visual memory or visuospatial function. The extent to which cognition improved was correlated with the change in CD4 cell count following ART, suggesting a link between cognitive outcome and immune system integrity. Together, the present findings indicate that the neuropsychological test performance of most HIV patients taking ART is comparable to those not taking ART. Development of pharmaceutical treatments and rehabilitation strategies that target the cognitive effects of HIV infection is needed.

Specificity of verbal learning impairment and recovery in a marijuana-dependent male: the effects of sustained marijuana abstinence.

INTRODUCTION: We present the case of a young adult in treatment for marijuana dependence, with recurrent depression and a history of possible traumatic brain injury, complaining of concentration, memory, and initiation problems. METHODS: The patient was assessed longitudinally during treatment with a brief neuropsychological battery, as well as measures of marijuana use and mood. RESULTS: Testing at treatment baseline revealed performance that was generally in the high average range or higher on measures of reaction time and attention, with a selective impairment in verbal learning (borderline to extremely low range). Following 8 weeks of abstinence from marijuana, his verbal learning recovered to expected levels (high average range), with signs of improved learning strategy, efficiency, rate, and capacity. However, his reaction time and attention showed minimal evidence of change. CONCLUSIONS: This finding is consistent with the literature that demonstrates that marijuana-associated neurocognitive impairments may be most pronounced in the domain of verbal learning and may remit with abstinence. The clinical implications of these case findings and recommendations for neuropsychological assessment of marijuana abusers are discussed.

In-Materio Reservoir Computing in a Sulfonated Polyaniline Network.

A sulfonated polyaniline (SPAN) organic electrochemical network device (OEND) is fabricated using a simple drop-casting method on multiple Au electrodes for use in reservoir computing (RC). The SPAN network has humidity-dependent electrical properties. Under high humidity, the SPAN OEND exhibits mainly ionic conduction, including charging of an electric double layer and ionic diffusion. The nonlinearity and hysteresis of the current-voltage characteristics progressively increase with increasing humidity. The rich dynamic output behavior indicates wide variations for each electrode, which improves the RC performance because of the disordered network. For RC, waveform generation and short-term memory tasks are realized by a linear combination of outputs. The waveform task accuracy and memory capacity calculated from a short-term memory task reach 90% and 33.9, respectively. Improved spoken-digit classification is realized with 60% accuracy by only 12 outputs, demonstrating that the SPAN OEND can manage time series dynamic data operation in RC owing to a combination of rich dynamic and nonlinear electronic properties. The results suggest that SPAN-based electrochemical systems can be applied for material-based computing, by exploiting their intrinsic physicochemical behavior.

Direct patterning of covalent organic monolayers on silicon using nanoimprint lithography.

Two fabrication schemes are reported for the direct patterning of organic monolayers on oxide-free silicon, combining top-down nanoimprint lithography and bottom-up monolayer formation. The first approach was designed to form monolayer patterns on the imprinted areas, while the second approach was designed for monolayer formation outside of the imprinted features. By both approaches, covalently bonded Si-C monolayer patterns with feature sizes ranging from 100 nm to 100 microm were created via a hydrosilylation procedure using diluted reagents. Both unfunctionalized and omega-functionalized alkenes were patterned successfully.

Low-temperature solution synthesis of chemically functional ferromagnetic FePtAu nanoparticles.

Magnetic nanoparticles are of great scientific and technological interest. The application of ferromagnetic nanoparticles for high-density data storage has great potential, but energy efficient synthesis of uniform, isolated, and patternable nanoparticles that remain ferromagnetic at room temperature is not trivial. Here, we present a low-temperature solution synthesis method for FePtAu nanoparticles that addresses all those issues and therefore can be regarded as an important step toward applications. We show that the onset of the chemically ordered face-centered tetragonal (L1(0)) phase is obtained for thermal annealing temperatures as low as 150 degrees C. Large uniaxial magnetic anisotropy (10(7) erg/cm(3)) and a high long-range order parameter have been obtained. Our low-temperature solution annealing leaves the organic ligands intact, so that the possibility for postanneal monolayer formation and chemically assisted patterning on a surface is maintained.

Monolayer-directed assembly and magnetic properties of FePt nanoparticles on patterned aluminum oxide.

FePt nanoparticles (NPs) were assembled on aluminum oxide substrates, and their ferromagnetic properties were studied before and after thermal annealing. For the first time, phosph(on)ates were used as an adsorbate to form self-assembled monolayers (SAMs) on alumina to direct the assembly of NPs onto the surface. The Al(2)O(3) substrates were functionalized with aminobutylphosphonic acid (ABP) or phosphonoundecanoic acid (PNDA) SAMs or with poly(ethyleneimine) (PEI) as a reference. FePt NPs assembled on all of these monolayers, but much less on unmodified Al(2)O(3), which shows that ligand exchange at the NPs is the most likely mechanism of attachment. Proper modification of the Al(2)O(3) surface and controlling the immersion time of the modified Al(2)O(3) substrates into the FePt NP solution resulted in FePt NPs assembly with controlled NP density. Alumina substrates were patterned by microcontact printing using aminobutylphosphonic acid as the ink, allowing local NP assembly. Thermal annealing under reducing conditions (96%N(2)/4%H(2)) led to a phase change of the FePt NPs from the disordered FCC phase to the ordered FCT phase. This resulted in ferromagnetic behavior at room temperature. Such a process can potentially be applied in the fabrication of spintronic devices.

A deep-learning approach to realizing functionality in nanoelectronic devices.

Many nanoscale devices require precise optimization to function. Tuning them to the desired operation regime becomes increasingly difficult and time-consuming when the number of terminals and couplings grows. Imperfections and device-to-device variations hinder optimization that uses physics-based models. Deep neural networks (DNNs) can model various complex physical phenomena but, so far, are mainly used as predictive tools. Here, we propose a generic deep-learning approach to efficiently optimize complex, multi-terminal nanoelectronic devices for desired functionality. We demonstrate our approach for realizing functionality in a disordered network of dopant atoms in silicon. We model the input-output characteristics of the device with a DNN, and subsequently optimize control parameters in the DNN model through gradient descent to realize various classification tasks. When the corresponding control settings are applied to the physical device, the resulting functionality is as predicted by the DNN model. We expect our approach to contribute to fast, in situ optimization of complex (quantum) nanoelectronic devices.