When conducting a single-molecule measurement, data are analyzed using the histogram of a measured physical quantity in which a single dataset contains information about a specific single molecule. Oftentimes, the histogram consisting of only specific single-molecule information excludes the input from other information sources. In other words, despite measuring the single molecule during analysis, we miss many of the properties of that single molecule. Herein, we have successfully identified a single molecule with a high degree of precision via a one-electric current pulse method using machine learning to read the single-molecule information. With the use of positive unlabeled classification, which is one of the techniques used in machine learning, we have developed a method for discerning a single molecule from a background of electric noises by analyzing the electric noises produced at the nanoscale level. In this method, we have demonstrated that the 2-, 3-, and 4-type nucleotides could be identified with a high degree of accuracy at a single-molecule resolution. This method can be widely applied for the accurate identification of a nucleotide using one measurement signal within a noisy matrix.

A highly atom-economical enantioselective Rauhut–Currier and [3+2] annulation has been established by flow system and machine-learning-assisted exploration of suitable conditions.

More specifically, whereas the noise in nanopore systems was elucidated to stem from several sources such as surface charge fluctuations and dielectric loss, coupling between the device capacitance and the voltage noise in current amplifiers was found to be the most significant factor giving rise to high-frequency noise above 10 kHz. [25][26][27] Previous nanopore measurements often used low-pass filters to cut-off this fast noise for detecting resistive pulse signals, which has proven useful in studying translocation dynamics of small molecules. [28,29] Nonetheless, as the sensitivity of the nanosensor was improved by employing ultrathin membranes such as graphene [30,31] and MoS 2 , [19] it started to become possible to probe not only the size of objects but their shapes, surface charge densities, and even mass from the ionic current signals. [8,32] In this context, it turns out to be an important issue to reduce the high frequency noise without any pre-filters as it generally involves signal blunting that obscures the fine yet important ionic current profiles. One of the effective strategies was to employ highly insulating materials as low-capacitance substrates, for example quartz and polymers. [33] Later, it was also found that covering the membrane surface with a thick polymer layer can serve to reduce the noise. [34] These works indeed provided nanopore chip designs for diminishing the current fluctuations by more than an order of magnitude through tailoring surface reactions and capacitive coupling. [27,[34][35][36][37] Besides the material and device engineering for controlling the physical/chemical phenomena relevant to ionic current fluctuations, digital post processing has been utilized to mitigate the noise via band-pass filtering and wave transformations. [38,39] However, previous studies have found it to be a non-trivial task since the computation in frequency domains inevitably entails signal distortions thereby obscured the small yet important features occurring at variable time scales due to the stochastic and random nature of the translocation dynamics. [39] Here we report on a novel concept for post-denoising of ionic current in solid-state nanopores. Our method is based on a deep learning algorithm formulated to extract noise floor from ionic current (I ion ) versus time (t) curves without clean data (Figure 1). This strategy is known as Noise2Noise [40] proven to be useful in processing noisy digital images. [41] In the present study, we exploited it to demonstrate clarification of fine signatures in a resistive pulse signal that may otherwise be immersed in noise or completely smeared out under the conventional signal processing.Noise is ubiquitous in real space that hinders detection of minute yet important signals in electrical sensors. Here, the authors report on a deep learning approach for denoising ionic current in resistive pulse sensing. Electrophoretically-driven translocation motions of single-nanoparticles in a nanocorrugated nanopore are detected. The noise is reduced by a convo...

A Highly efficient synthesis of α-ketiminophosphonates has been established for the electrochemical oxidation of α-amino phosphonates with the utilization of machine-learning-assisted simultaneous multiparameter screening. After brief experimental screening, the Bayesian...

Traditional optimization methods using one variable at a time approach waste time and chemicals and assume that different parameters are independent from one another. Hence, a simpler, more practical, and rapid process for predicting reaction conditions that can be applied to several manufacturing environmentally sustainable processes is highly desirable. In this study, biaryl compounds were synthesized efficiently using an organic Brønsted acid catalyst in a flow system. Bayesian optimization-assisted multi-parameter screening, which employs one-hot encoding and appropriate acquisition function, rapidly predicted the suitable conditions for the synthesis of 2-amino-2′-hydroxy-biaryls (maximum yield of 96%). The established protocol was also applied in an optimization process for the efficient synthesis of 2,2′-dihydroxy biaryls (up to 97% yield). The optimized reaction conditions were successfully applied to gram-scale synthesis. We believe our algorithm can be beneficial as it can screen a reactor design without complicated quantification and descriptors.

In article number 2100191, Makusu Tsutsui, Tomoji Kawai, Takashi Washio, and co‐workers demonstrated the use of deep learning in nanopore sensing. Employing convolutional neural networks, ionic current was denoised in a high dimensional feature space that enabled to visualize hidden signals reflecting fast translocation motions of objects in a nanoscale conduit. This deep denoising paves a way for single molecule fingerprinting by ionic current using solid state nanopores.

No abstract

Correction for ‘Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence’ by Masaru Kondo et al., Chem. Commun., 2020, 56, 1259–1262, DOI: 10.1039/C9CC08526B.