The brain is capable of massively parallel information processing while consuming only ∼1-100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (<10 pJ for 10 μm devices), displays >500 distinct, non-volatile conductance states within a ∼1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.

No abstract

published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

A variety of optical applications rely on the absorption and reemission of light. The quantum yield of this process often plays an essential role. When the quantum yield deviates from unity by significantly less than 1%, applications such as luminescent concentrators and optical refrigerators become possible. To evaluate such high performance, we develop a measurement technique for luminescence efficiency with sufficient accuracy below one part per thousand. Photothermal threshold quantum yield is based on the quantization of light to minimize overall measurement uncertainty. This technique is used to guide a procedure capable of making ensembles of near-unity emitting cadmium selenide/cadmium sulfide (CdSe/CdS) core-shell quantum dots. We obtain a photothermal threshold quantum yield luminescence efficiency of 99.6 ± 0.2%, indicating nearly complete suppression of nonradiative decay channels.

The interface between cells and non-biological surfaces regulates cell attachment, chronic tissue responses, and ultimately the success of medical implants or biosensors. Clinical and laboratory studies show that topological features of the surface profoundly influences cellular responses, e.g. titanium surfaces with nano- and microtopographical structures enhance osteoblast attachment and host-implant integration as compare to smooth surface. To understand how cells and tissues respond to different topographical features, it is of critical importance to directly visualize the cell-materials interface at the relevant nanometer length scale. Here, we present a new method for in situ examination of the cell-to-material interface at any desired location, based on focused-ion beam milling and scanning electron microscopy imaging (FIB-SEM) to resolve the cell membrane-to-material interface with 10 nm resolution. By examining how cell membranes interact with topographical features such as nanoscale protrusions or invaginations, we discovered that the cell membrane readily deforms inward and wraps around protruding structures, but hardly deforms outward to contour invaginating structures. This asymmetric membrane response (inward vs. outward deformation) causes the cleft width between the cell membrane and the nanostructure surface to vary for more than an order of magnitude. Our results suggest that surface topology is a crucial consideration for the development of medical implants or biosensors whose performances are strongly influenced by the cell-to-material interface. We anticipate that the method can be used to explore the direct interaction of cells/tissue with medical devices such as metal implants in the future.

DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

Organic electrochemical transistors (OECTs) show great promise for flexible, low‐cost, and low‐voltage sensors for aqueous solutions. The majority of OECT devices are made using the polymer blend poly(ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS), in which PEDOT is intrinsically doped due to inclusion of PSS. Because of this intrinsic doping, PEDOT:PSS OECTs generally operate in depletion mode, which results in a higher power consumption and limits stability. Here, a straightforward method to de‐dope PEDOT:PSS using commercially available amine‐based molecular de‐dopants to achieve stable enhancement‐mode OECTs is presented. The enhancement‐mode OECTs show mobilities near that of pristine PEDOT:PSS (≈2 cm2 V−1 s−1) with stable operation over 1000 on/off cycles. The electron and proton exchange among PEDOT, PSS, and the molecular de‐dopants are characterized to reveal the underlying chemical mechanism of the threshold voltage shift to negative voltages. Finally, the effect of the de‐doping on the microstructure of the spin‐cast PEDOT:PSS films is investigated.

units (GPUs) and >1000 central processing units (CPUs) for some of the highest performance demonstrations. [4] In order to approach the massively parallel and energy efficient operation of the brain (≈25 W), [7] neuromorphic computing architectures have been proposed which utilize physical processes in materials to emulate synaptic behavior. [8][9][10] Among these, resistive memory (memristive) devices [11,12] have gained traction as a highly suitable option, offering projected efficiency gains of up to 10 6 over von Neumann architectures when implementing ANN algorithms. [13,14] Efficiency gains originate from parallel computation and scale with array size (N) [15] : an N × N sized neuromorphic array can simultaneously perform N 2 operations using Ohm's and Kirchoff's laws (multiply and accumulate, respectively), whereas GPUs can perform only N operations simultaneously.Nonvolatile memristive devices such as phase change memory (PCM) and resistive random-access memory (ReRAM) have been proposed for use as nonvolatile artificial synapses due to their ability to tune the resistance state by applied voltage pulses. [16][17][18] By arranging these devices in a crossbar array architecture, vector-matrix multiplication (VMM) can be performed in an analog fashion where the input vector is represented by voltages, the operator matrix is represented by the conductance of each memristive element, and the output vector is represented by currents. These crossbar arrays have recently been implemented in a dot-product engine which uses VMM operations to classify handwritten digits with ≈90% accuracy. [19] However, this demonstration requires a timeconsuming feedback programming scheme which reduces the parallelism of the write operation to the array and ultimately its overall efficiency.Recently, electrochemical nonvolatile redox memory (NVRM) devices [20,21] have emerged as an ideal candidate for neuromorphic arrays as they decouple read and write operations, thereby allowing for low-energy programming and accurately tunable conductance states while potentially avoiding the time-voltage dilemma. [22,23] Furthermore, organic materials are an attractive alternative to conventional resistive memory due to their linearly tunable conductance, [22] biocompatibility, [24] and unique switching mechanisms which significantly differ from their inorganic counterparts. [25][26][27][28][29][30] In particular, there has been interest in mixed ionic-electronic conductors, Recent breakthroughs in artificial neural networks (ANNs) have spurred interest in efficient computational paradigms where the energy and time costs for training and inference are reduced. One promising contender for efficient ANN implementation is crossbar arrays of resistive memory elements that emulate the synaptic strength between neurons within the ANN. Organic nonvolatile redox memory has recently been demonstrated as a promising device for neuromorphic computing, offering a continuous range of linearly programmable resistance states and tunable electronic and elect...