Synergistic D-Amino Acids Based Antimicrobial Cocktails Formulated via High-Throughput Screening and Machine Learning.

Antimicrobial resistance (AMR) from pathogenic bacterial biofilms has become a global health issue while developing novel antimicrobials is inefficient and costly. Combining existing multiple drugs with enhanced efficacy and/or reduced toxicity may be a promising approach to treat AMR. D-amino acids mixtures coupled with antibiotics can provide new therapies for drug-resistance infection with reduced toxicity by lower drug dosage requirements. However, iterative trial-and-error experiments are not tenable to prioritize credible drug formulations, owing to the extremely large number of possible combinations. Herein, a new avenue is provide to accelerate the exploration of desirable antimicrobial formulations via high-throughput screening and machine learning optimization. Such an intelligent method can navigate the large search space and rapidly identify the D-amino acid mixtures with the highest anti-biofilm efficiency and also the synergisms between D-amino acid mixtures and antibiotics. The optimized drug cocktails exhibit high antimicrobial efficacy while remaining non-toxic, which is demonstrated not only from in vitro assessments but also the first in vivo study using a lung infection mouse model.

Confined Liquid-Phase Growth of Crystalline Compound Semiconductors on Any Substrate.

The growth of crystalline compound semiconductors on amorphous and non-epitaxial substrates is a fundamental challenge for state-of-the-art thin-film epitaxial growth techniques. Direct growth of materials on technologically relevant amorphous surfaces, such as nitrides or oxides results in nanocrystalline thin films or nanowire-type structures, preventing growth and integration of high-performance devices and circuits on these surfaces. Here, we show crystalline compound semiconductors grown directly on technologically relevant amorphous and non-epitaxial substrates in geometries compatible with standard microfabrication technology. Furthermore, by removing the traditional epitaxial constraint, we demonstrate an atomically sharp lateral heterojunction between indium phosphide and tin phosphide, two materials with vastly different crystal structures, a structure that cannot be grown with standard vapor-phase growth approaches. Critically, this approach enables the growth and manufacturing of crystalline materials without requiring a nearly lattice-matched substrate, potentially impacting a wide range of fields, including electronics, photonics, and energy devices.

Mimicking Biological Synaptic Functionality with an Indium Phosphide Synaptic Device on Silicon for Scalable Neuromorphic Computing.

Neuromorphic or "brain-like" computation is a leading candidate for efficient, fault-tolerant processing of large-scale data as well as real-time sensing and transduction of complex multivariate systems and networks such as self-driving vehicles or Internet of Things applications. In biology, the synapse serves as an active memory unit in the neural system and is the component responsible for learning and memory. Electronically emulating this element via a compact, scalable technology which can be integrated in a three-dimensional (3-D) architecture is critical for future implementations of neuromorphic processors. However, present day 3-D transistor implementations of synapses are typically based on low-mobility semiconductor channels or technologies that are not scalable. Here, we demonstrate a crystalline indium phosphide (InP)-based artificial synapse for spiking neural networks that exhibits elasticity, short-term plasticity, long-term plasticity, metaplasticity, and spike timing-dependent plasticity, emulating the critical behaviors exhibited by biological synapses. Critically, we show that this crystalline InP device can be directly integrated via back-end processing on a Si wafer using a SiO2 buffer without the need for a crystalline seed, enabling neuromorphic devices that can be implemented in a scalable and 3-D architecture. Specifically, the device is a crystalline InP channel field-effect transistor that interacts with neuron spikes by modification of the population of filled traps in the MOS structure itself. Unlike other transistor-based implementations, we show that it is possible to mimic these biological functions without the use of external factors (e.g., surface adsorption of gas molecules) and without the need for the high electric fields necessary for traditional flash-based implementations. Finally, when exposed to neuronal spikes with a waveform similar to that observed in the brain, these devices exhibit the ability to learn without the need for any external potentiating/depressing circuits, mimicking the biological process of Hebbian learning.