Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.

In this work, ultrathin Ti3C2-MXene nanosheets were synthesized by minimally intensive layer delamination methods, and uniformly functionalized with aminosilane (f-Ti3C2-MXene) to provide a covalent binding for the immobilized bio-receptor (anti-CEA) for label free, ultrasensitive detection of cancer biomarker (carcinoembryonic antigen, CEA). The effect of different redox probes on the electrochemical behavior of f-Ti3C2-MXene was investigated and found that hexaammineruthenium ([Ru(NH3)6]3+) is the preferable redox probe for biosensing. The fabricated biofunctionalized Ti3C2-MXene exhibits a linear detection range of 0.0001-2000 ng mL-1 with sensitivity of 37.9 µA ng-1 mL cm-2 per decade. The wider linear detection range of our f-Ti3C2-MXene is not only higher than previously reported pristine 2D nanomaterials, but is even comparable to other hybrid 2D nanomaterials. We believe that this work opens a new window for development of MXene-based highly sensitive DNA, aptamer, enzyme, antibody, and cell based biosensors, and could be further used in drug delivery application.

Conventional SO₂ scrubbing agents, namely calcium oxide and zeolites, are often used to remove SO₂ using a strong or irreversible adsorption-based process. However, adsorbents capable of sensing and selectively capturing this toxic molecule in a reversible manner, with in-depth understanding of structure–property relationships, have been rarely explored. Here we report the selective removal and sensing of SO₂ using recently unveiled fluorinated metal–organic frameworks (MOFs). Mixed gas adsorption experiments were performed at low concentrations ranging from 250 p.p.m. to 7% of SO₂. Direct mixed gas column breakthrough and/or column desorption experiments revealed an unprecedented SO₂ affinity for KAUST-7 (NbOFFIVE-1-Ni) and KAUST-8 (AlFFIVE-1-Ni) MOFs. Furthermore, MOF-coated quartz crystal microbalance transducers were used to develop sensors with the ability to detect SO₂ at low concentrations ranging from 25 to 500 p.p.m.

Various hypotheses of information representation in brain, referred to as neural codes, have been proposed to explain the information transmission between neurons. Neural coding plays an essential role in enabling the brain-inspired spiking neural networks (SNNs) to perform different tasks. To search for the best coding scheme, we performed an extensive comparative study on the impact and performance of four important neural coding schemes, namely, rate coding, time-to-first spike (TTFS) coding, phase coding, and burst coding. The comparative study was carried out using a biological 2-layer SNN trained with an unsupervised spike-timing-dependent plasticity (STDP) algorithm. Various aspects of network performance were considered, including classification accuracy, processing latency, synaptic operations (SOPs), hardware implementation, network compression efficacy, input and synaptic noise resilience, and synaptic fault tolerance. The classification tasks on Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets were applied in our study. For hardware implementation, area and power consumption were estimated for these coding schemes, and the network compression efficacy was analyzed using pruning and quantization techniques. Different types of input noise and noise variations in the datasets were considered and applied. Furthermore, the robustness of each coding scheme to the non-ideality-induced synaptic noise and fault in analog neuromorphic systems was studied and compared. Our results show that TTFS coding is the best choice in achieving the highest computational performance with very low hardware implementation overhead. TTFS coding requires 4x/7.5x lower processing latency and 3.5x/6.5x fewer SOPs than rate coding during the training/inference process. Phase coding is the most resilient scheme to input noise. Burst coding offers the highest network compression efficacy and the best overall robustness to hardware non-idealities for both training and inference processes. The study presented in this paper reveals the design space created by the choice of each coding scheme, allowing designers to frame each scheme in terms of its strength and weakness given a designs’ constraints and considerations in neuromorphic systems.