Supplementary document for Metasurface Enabled Barcoding for Compact Flow Cytometry - 6831134.pdf

Supplemental Documen

Heat Flow Guiding and Modulation by Kinks in a Silicon Nanoribbon

Tailoring heat flow in solids has profound implications for the innovation of functional thermal devices. However, the current methods face technological challenges related to system complexity, material stability, and operating temperature. In this study, we demonstrated efficient heat flow modulation in a single material without a phase transition, using a simple and entirely material-independent strategy, kinked nanostructure patterning, at near-ambient temperature. By carefully controlling the kink arm length and kink angle of the Si nanoribbons, we achieved a thermal conductivity modulation of up to ∼20%. Our theoretical modeling showed that this modulation results from the competing roles of phonon backscattering and open view channels on heat transport. We also build a regime map based on the existence of an open view channel and provide concrete design guidelines for thermal conductivity modulation considering the kink angle and arm length. This study opens up new opportunities for efficient heat flow manipulation through nanostructure patterning

Characterization of a Chiral Enolate Aggregate and Observation of ⁶Li−¹H Scalar Coupling

A chiral enolate aggregate 1 containing a lithium enolate and a chiral lithium amide was systematically investigated by various NMR techniques. 1H and 13C DOSY at 25 and −78 °C provide its solution structure, aggregation number, and formula weight. Multiple 2D 6Li NMR techniques, such as 6Li−6Li EXSY, 6Li−1H HOESY, were utilized to investigate its stereochemical structure. The configuration of the enolate in complex 1 was confirmed by 6Li−1H HOESY and trapping with TMS−Cl. A unique 6Li−1H coupling through the Li−N−C−H network was observed. This scalar coupling was corroborated by 6Li−1H HMQC, deuterium labeling experiments, and selective 1H decoupling 6Li NMR. The stereostructure of 1 provides a model for the origin of enantioselectivity of chiral lithium amide-induced enolate addition reactions

Formula Weight Prediction by Internal Reference Diffusion-Ordered NMR Spectroscopy (DOSY)

Formula weight (FW) information is important to characterize the composition, aggregation number, and solvation state of reactive intermediates and organometallic complexes. We describe an internal reference correlated DOSY method for calculating the FW of unknown species in different solvents with different concentrations. Examples for both the small molecule (DIPA) and the organometallic complex (aggregate 1) yield excellent correlations. We also found the relative diffusion rate is inversely proportional to the viscosity change of the solution, which is consistent with the theoretical Stokes−Einstein equation. The accuracy of the least-squares linear prediction r2 and the percentage difference of FW prediction are directly related to the density change; greater accuracy was observed with decreasing density. We also discuss the guidelines and other factors for successful application of this internal reference correlated DOSY method. This practical method can be conveniently modified and applied to the characterization of other unknown molecules or complexes

Analysis of an Asymmetric Addition with a 2:1 Mixed Lithium Amide/<i>n</i>-Butyllithium Aggregate

A 2:1 lithium amide/n-butyllithium aggregate 1 is investigated as an asymmetric addition template in hydrocarbon solvents. Several different chiral lithium amides were synthesized from l-valine and tested in the asymmetric addition of n-BuLi to various aldehydes. Enantiomeric excesses up to 83% were obtained in the case of the addition of n-BuLi to pivaldehyde at −116 °C in pentane. 1H and 13C INEPT DOSY were utilized to characterize a new trimeric complex 12 between 2 equiv of lithium amide and 1 equiv of lithium alkoxide. This mixed aggregate strongly indicates the possibility of product-induced chirality inhibition that is detrimental to the enantioselectivity of asymmetric addition reaction

Well-Dispersed High-Loading Pt Nanoparticles Supported by Shell−Core Nanostructured Carbon for Methanol Electrooxidation

Shell−core nanostructured carbon materials with a nitrogen-doped graphitic layer as a shell and pristine carbon black particle as a core were synthesized by carbonizing the hybrid materials containing in situ polymerized aniline onto carbon black. In an N-doped carbon layer, the nitrogen atoms substitute carbon atoms at the edge and interior of the graphene structure to form pyridinic N and quaternary N structures, respectively. As a result, the carbon structure becomes more compact, showing curvatures and disorder in the graphene stacking. In comparison with nondoped carbon, the N-doped one was proved to be a suitable supporting material to synthesize high-loading Pt catalysts (up to 60 wt %) with a more uniform size distribution and stronger metal−support interactions due to its high electrochemically accessible surface area, richness of disorder and defects, and high electron density. Moreover, the more rapid charge-transfer rates over the N-doped carbon material are evidenced by the high crystallinity of the graphitic shell layer with nitrogen doping as well as the low charge-transfer resistance at the electrolyte/electrode interface. Beneficial roles of nitrogen doping can be found to enhance the CO tolerance of Pt catalysts. Accordingly, an improved performance in methanol oxidation was achieved on a high-loading Pt catalyst supported by N-doped carbon. The enhanced catalytic properties were extensively discussed based on mass activity (Pt utilization) and intrinsic activity (charge-transfer rate). Therefore, N-doped carbon layers present many advantages over nondoped ones and would emerge as an interesting supporting carbon material for fuel cell electrocatalysts

Characterization of Dimeric Chiral Lithium Amide Structures Derived from <i>N</i>-isopropyl-<i>O</i>- triisopropylsilyl Valinol

The dimeric structure is characterized for a chiral amide base complex consisting of an (S)-N-isopropyl-O-triisopropylsilyl valinol ligand and lithium. The complex is characterized by a variety of NMR techniques, including multinuclear one- and two-dimensional NMR experiments and diffusion-ordered NMR spectroscopy (DOSY) as well as diffusion coefficient-formula weight (D-fw) correlation analyses. Spartan calculations are presented which support the structural assignment. This structural characterization leads to an explanation of the behavior and the reactivity of these complexes in solution

¹³C INEPT Diffusion-Ordered NMR Spectroscopy (DOSY) with Internal References

13C INEPT Diffusion-ordered NMR spectroscopy (DOSY) with an internal reference system was developed to study the aggregation state of THF-solvated LDA dimeric complex. Six components are clearly identified in the diffusion dimension, and their DOSY-generated 13C INEPT spectrum slices agree extremely well with their respective INEPT spectra. The correlation between log D and log FW of the linear least-squares fit to reference points of all components is exceptionally high:  (r = 0.9985)

The employment impact of emerging digital technologies

This paper measures the exposure of industries and occupations to 40 digital technologies that emerged over the past decade and estimates their impact on European employment. Using a novel approach that leverages sentence transformers, we calculate exposure scores based on the semantic similarity between patents and ISCO-08/NACE Rev.2 classifications to construct an open–access database, ‘TechXposure’. By combining our data with a shift–share approach, we instrument the regional exposure to emerging digital technologies to estimate their employment impact across European regions. We find an overall positive effect of emerging digital technologies on employment, with a one-standard-deviation increase in regional exposure leading to a 1.069 percentage point increase in the employment-to-population ratio. However, upon examining the individual effects of these technologies, we find that smart agriculture, the internet of things, industrial and mobile robots, digital advertising, mobile payment, electronic messaging, cloud storage, social network technologies, and machine learning negatively impact regional employment.</p

The employment impact of emerging digital technologies

This paper measures the exposure of industries and occupations to 40 digital technologies that emerged over the past decade and estimates their impact on European employment. Using a novel approach that leverages sentence transformers, we calculate exposure scores based on the semantic similarity between patents and ISCO-08/NACE Rev.2 classifications to construct an open–access database, ‘TechXposure’. By combining our data with a shift–share approach, we instrument the regional exposure to emerging digital technologies to estimate their employment impact across European regions. We find an overall positive effect of emerging digital technologies on employment, with a one-standard-deviation increase in regional exposure leading to a 1.069 percentage point increase in the employment-to-population ratio. However, upon examining the individual effects of these technologies, we find that smart agriculture, the internet of things, industrial and mobile robots, digital advertising, mobile payment, electronic messaging, cloud storage, social network technologies, and machine learning negatively impact regional employment.</p