Selective Perchlorate Sensing Using Electrochemical Impedance Spectroscopy with Self-Assembled Monolayers of semiaza-Bambusurils.

In the last two decades, perchlorate salts have been identified as environmental pollutants and recognized as potential substances affecting human health. We describe self-assembled monolayers (SAMs) of novel semiaza-bambus[6]urils (semiaza-BUs) equipped with thioethers or disulfide (dithiolane) functionalities as surface-anchoring groups on gold electrodes. Cyclic voltammetry (CV) with Fe(CN)6 3-/4- as a redox probe, together with X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM) and ellipsometry, were employed to characterize the interactions at the interface between the anchoring groups and the metal substrate. Data showed that the anion receptors' packing on the gold strongly depends on the anchoring group. As a result, SAMs of BUs with lipoic amide side chains show a concentration-dependent layer thickness. The BU SAMs are extremely stable on repeated electrochemical potential scans and can selectively recognize perchlorate anions. Our electrochemical impedance spectroscopy (EIS) studies indicated that semiaza-BU equipped with the lipoic amide side chains binds perchlorate (2-100 mM) preferentially over other anions such as F- , Cl- , I- , AcO- , H2 PO4 - , HPO4 2- , SO4 2- , NO2 - , NO3 - , or CO3 2- . The resistance performance is 10 to 100 times more efficient than SAMs containing all other tested anions.

Dynamic molecular switches with hysteretic negative differential conductance emulating synaptic behaviour.

To realize molecular-scale electrical operations beyond the von Neumann bottleneck, new types of multifunctional switches are needed that mimic self-learning or neuromorphic computing by dynamically toggling between multiple operations that depend on their past. Here, we report a molecule that switches from high to low conductance states with massive negative memristive behaviour that depends on the drive speed and number of past switching events, with all the measurements fully modelled using atomistic and analytical models. This dynamic molecular switch emulates synaptic behavior and Pavlovian learning, all within a 2.4-nm-thick layer that is three orders of magnitude thinner than a neuronal synapse. The dynamic molecular switch provides all the fundamental logic gates necessary for deep learning because of its time-domain and voltage-dependent plasticity. The synapse-mimicking multifunctional dynamic molecular switch represents an adaptable molecular-scale hardware operable in solid-state devices, and opens a pathway to simplify dynamic complex electrical operations encoded within a single ultracompact component.