Abnormal Silicon Etching Behaviors in Nanometer-Sized Channels.

Modern semiconductor fabrication is challenged by difficulties in overcoming physical and chemical constraints. A major challenge is the wet etching of dummy gate silicon, which involves the removal of materials inside confined spaces of a few nanometers. These chemical processes are significantly different in the nanoscale and bulk. Previously, electrical double-layer formation, bubble entrapment, poor wettability, and insoluble intermediate precipitation have been proposed. However, the exact suppression mechanisms remain unclear due to the lack of direct observation methods. Herein, we investigate limiting factors for the etching kinetics of silicon with tetramethylammonium hydroxide at the nanoscale by using liquid-phase transmission electron microscopy, three-dimensional electron tomography, and first-principles calculations. We reveal suppressed chemical reactions, unstripping phenomena, and stochastic etching behaviors that have never been observed on a macroscopic scale. We expect that solutions can be suggested from this comprehensive insight into the scale-dependent limiting factors of fabrication.

Surface-functionalized SERS platform for deep learning-assisted diagnosis of Alzheimer's disease.

Early diagnosis of Alzheimer's disease is crucial to stall the deterioration of brain function, but conventional diagnostic methods require complicated analytical procedures or inflict acute pain on the patient. Then, label-free Surface-enhanced Raman spectroscopy (SERS) analysis of blood-based biomarkers is a convenient alternative to rapidly obtain spectral information from biofluids. However, despite the rapid acquisition of spectral information from biofluids, it is challenging to distinguish spectral features of biomarkers due to interference from biofluidic components. Here, we introduce a deep learning-assisted, SERS-based platform for separate analysis of blood-based amyloid ß (1-42) and metabolites, enabling the diagnosis of Alzheimer's disease. SERS substrates consisting of Au nanowire arrays are fabricated and functionalized in two characteristic ways to compare the validity of different Alzheimer's disease biomarkers measured on our SERS system. The 6E10 antibody is immobilized for the capture of amyloid ß (1-42) and analysis of its oligomerization process, while various self-assembled monolayers are attached for different dipole interactions with blood-based metabolites. Ultimately, SERS spectra of blood plasma of Alzheimer's disease patients and human controls are measured on the substrates and classified via advanced deep learning techniques that automatically extract informative features to learn generalizable representations. Accuracies up to 99.5% are achieved for metabolite-based analyses, which are verified with an explainable artificial intelligence technique that identifies key spectral features used for classification and for deducing significant biomarkers.

Separation-free bacterial identification in arbitrary media via deep neural network-based SERS analysis.

Universal and fast bacterial detection technology is imperative for food safety analyses and diagnosis of infectious diseases. Although surface-enhanced Raman spectroscopy (SERS) has recently emerged as a powerful solution for detecting diverse microorganisms, its widespread application has been hampered by strong signals from surrounding media that overwhelm target signals and require time-consuming and tedious bacterial separation steps. By using SERS analysis boosted with a newly proposed deep learning model named dual-branch wide-kernel network (DualWKNet), a markedly simpler, faster, and effective route to classify signals of two common bacteria E. coli and S. epidermidis and their resident media without any separation procedures is demonstrated. With outstanding classification accuracies up to 98%, the synergistic combination of SERS and deep learning serves as an effective platform for "separation-free" detection of bacteria in arbitrary media with short data acquisition times and small amounts of training data.

Synergistic Integration of Chemo-Resistive and SERS Sensing for Label-Free Multiplex Gas Detection.

Practical sensing applications such as real-time safety alerts and clinical diagnoses require sensor devices to differentiate between various target molecules with high sensitivity and selectivity, yet conventional devices such as oxide-based chemo-resistive sensors and metal-based surface-enhanced Raman spectroscopy (SERS) sensors usually do not satisfy such requirements. Here, a label-free, chemo-resistive/SERS multimodal sensor based on a systematically assembled 3D cross-point multifunctional nanoarchitecture (3D-CMA), which has unusually strong enhancements in both "chemo-resistive" and "SERS" sensing characteristics is introduced. 3D-CMA combines several sensing mechanisms and sensing elements via 3D integration of semiconducting SnO2 nanowire frameworks and dual-functioning Au metallic nanoparticles. It is shown that the multimodal sensor can successfully estimate mixed-gas compositions selectively and quantitatively at the sub-100 ppm level, even for mixtures of gaseous aromatic compounds (nitrobenzene and toluene) with very similar molecular structures. This is enabled by combined chemo-resistive and SERS multimodal sensing providing complementary information.

Synergistic SERS Enhancement in GaN-Ag Hybrid System toward Label-Free and Multiplexed Detection of Antibiotics in Aqueous Solutions.

Noble metal-based surface-enhanced Raman spectroscopy (SERS) has enabled the simple and efficient detection of trace-amount molecules via significant electromagnetic enhancements at hot spots. However, the small Raman cross-section of various analytes forces the use of a Raman reporter for specific surface functionalization, which is time-consuming and limited to low-molecular-weight analytes. To tackle these issues, a hybrid SERS substrate utilizing Ag as plasmonic structures and GaN as charge transfer enhancement centers is presented. By the conformal printing of Ag nanowires onto GaN nanopillars, a highly sensitive SERS substrate with excellent uniformity can be fabricated. As a result, remarkable SERS performance with a substrate enhancement factor of 1.4 × 1011 at 10 fM for rhodamine 6G molecules with minimal spot variations can be realized. Furthermore, quantification and multiplexing capabilities without surface treatments are demonstrated by detecting harmful antibiotics in aqueous solutions. This work paves the way for the development of a highly sensitive SERS substrate by constructing complex metal-semiconductor architectures.

Reversible and Irreversible Responses of Defect-Engineered Graphene-Based Electrolyte-Gated pH Sensors.

We have studied the role of defects in electrolyte-gated graphene mesh (GM) field-effect transistors (FETs) by introducing engineered edge defects in graphene (Gr) channels. Compared with Gr-FETs, GM-FETs were characterized as having large increments of Dirac point shift (∼30-100 mV/pH) that even sometimes exceeded the Nernst limit (59 mV/pH) by means of electrostatic gating of H(+) ions. This feature was attributed to the defect-mediated chemisorptions of H(+) ions to the graphene edge, as supported by Raman measurements and observed cycling characteristics of the GM FETs. Although the H(+) ion binding to the defects increased the device response to pH change, this binding was found to be irreversible. However, the irreversible component showed relatively fast decay, almost disappearing after 5 cycles of exposure to solutions of decreasing pH value from 8.25 to 6.55. Similar behavior could be found in the Gr-FET, but the irreversible component of the response was much smaller. Finally, after complete passivation of the defects, both Gr-FETs and GM-FETs exhibited only reversible response to pH change, with similar magnitude in the range of 6-8 mV/pH.

Chemical and biological sensors based on defect-engineered graphene mesh field-effect transistors.

Graphene has been intensively studied for applications to high-performance sensors, but the sensing characteristics of graphene devices have varied from case to case, and the sensing mechanism has not been satisfactorily determined thus far. In this review, we describe recent progress in engineering of the defects in graphene grown by a silica-assisted chemical vapor deposition technique and elucidate the effect of the defects upon the electrical response of graphene sensors. This review provides guidelines for engineering and/or passivating defects to improve sensor performance and reliability.