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Mounting concerns regarding per‐/poly‐fluoroalkyl substances (PFAS) on human health are focusing attention on trace‐level PFAS detection in aqueous environments. Here, we report a readily prepared small molecule, 2,6‐bis(3,5‐diethyl‐1H‐pyrrol‐2‐yl)pyridine (receptor 1), that displays high binding affinities (logKa< = 4.9–6.2) and produces a strong “turn‐on” emission response when exposed to representative PFAS in hexanes. The hydrophobic nature of 1 , and its strong affinity for various PFAS, allowed hexanes solutions of 1 to be used as “turn‐on” emission sensors for dilute aqueous solutions of long‐chain (≥C8) PFAS under acidic conditions (pH 2) by liquid‐phase extraction (LPE). In the case of perfluorooctanoic acid (PFOA), the response was rapid (under 10 min) and sensitive. Limits of detection (LOD) as low as 250 ppt were readily achievable by direct naked‐eye observation. LOD as low as 40 and 100 ppt, respectively, could be reached for deionized and tap water solutions of PFOA using a smartphone color‐scanning application. Little change in the sensitivity was seen in the presence of a range of inorganic and organic species that could act as potential interferants. Support for the present findings came from UV–vis absorbance, fluorescence, 1 

This Letter reports a highly scaled 90 nm gate length β-Ga2O3 (Ga2O3) T-gate MOSFET with a power gain cutoff frequency (fMAX) of 55 GHz. The 60 nm thin epitaxial Ga2O3 channel layer was grown by molecular beam epitaxy, while the highly doped (n++) source/drain regions were regrown using metal organic chemical vapor deposition. Maximum on current (IDS,MAX) of 160 mA/mm and trans-conductance (gm) around 36 mS/mm were measured at VDS = 10 V for LSD = 1.5 μm device. Transconductance and on current are limited by high channel sheet resistance (Rsheet). Gate/drain breakdown voltage of 125 V was measured for LGD = 1.2 μm. We extracted 27 GHz current gain cutoff frequency (fT) and 55 GHz fMAX for 20 V drain bias for unpassivated devices. While no current collapse was seen initially for both drain and gate lag measurements for 500 ns pulse, moderate current collapse was observed after DC, RF measurements caused by electrical stressing. We calculated a high fT. VBR product of 3.375 THz V, which is comparable to the state-of-the-art GaN HEMTs. This figure of merit suggests that Ga2O3 could be a potential candidate for X-band application. 

We report on the growth of Si-doped homoepitaxial β-Ga2O3 thin films on (010) Ga2O3 substrates via metal-organic chemical vapor deposition (MOCVD) utilizing triethylgallium (TEGa) and trimethylgallium (TMGa) precursors. The epitaxial growth achieved an impressive 9.5 μm thickness at 3 μm/h using TMGa, a significant advance in material growth for electronic device fabrication. This paper systematically studies the Schottky barrier diodes fabricated on the three MOCVD-grown films, each exhibiting variations in the epilayer thickness, doping levels, and growth rates. The diode from the 2 μm thick Ga2O3 epilayer with TEGa precursor demonstrates promising forward current densities, the lowest specific on-resistance, and the lowest ideality factor, endorsing TEGa’s potential for MOCVD growth. Conversely, the diode from the 9.5 μm thick Ga2O3 layer with TMGa precursor exhibits excellent characteristics in terms of lowest leakage current, highest on-off ratio, and highest reverse breakdown voltage of −510 V without any electric field management, emphasizing TMGa’s suitability for achieving high growth rates in Ga2O3 epilayers for vertical power electronic devices. 

Traditional sentence embedding models encode sentences into vector representations to capture useful properties such as the semantic similarity between sentences. However, in addition to similarity, sentence semantics can also be interpreted via compositional operations such as sentence fusion or difference. It is unclear whether the compositional semantics of sentences can be directly reflected as compositional operations in the embedding space. To more effectively bridge the continuous embedding and discrete text spaces, we explore the plausibility of incorporating various compositional properties into the sentence embedding space that allows us to interpret embedding transformations as compositional sentence operations. We propose InterSent, an end-to-end framework for learning interpretable sentence embeddings that supports compositional sentence operations in the embedding space. Our method optimizes operator networks and a bottleneck encoder-decoder model to produce meaningful and interpretable sentence embeddings. Experimental results demonstrate that our method significantly improves the interpretability of sentence embeddings on four textual generation tasks over existing approaches while maintaining strong performance on traditional semantic similarity tasks.