Fabrication of Superhydrophobic–Hydrophilic Patterned Cu@Ag Composite SERS Substrate via Femtosecond Laser

Ultralow concentration molecular detection is critical in various fields,e.g.,food safety,environmental monitoring,and dis-ease diagnosis.Highly sensitive surface-enhanced Raman scattering(SERS)based on ultra-wettable surfaces has attracted attention due to its unique ability to detect trace molecules.However,the complexity and cost associated with the preparation of traditional SERS substrates restrict their practical application.Thus,an efficient SERS substrate preparation with high sensitivity,a simplified process,and controllable cost is required.In this study,a superhydrophobic–hydrophilic patterned Cu@Ag composite SERS substrate was fabricated using femtosecond laser processing technology combined with silver plating and surface modification treatment.By inducing periodic stripe structures through femtosecond laser processing,the developed substrate achieves uniform distribution hotspots.Using the surface wettability difference,the object to be measured can be confined in the hydrophilic region and the edge of the hydrophilic region,where the analyte is enriched by the coffee ring effect,can be quickly located by surface morphology difference of micro-nanostructures;thus,greatly improving detec-tion efficiency.The fabricated SERS substrate can detect Rhodamine 6G(R6G)at an extraordinarily low concentration of 10^(−15)mol/L,corresponding to an enhancement factor of 1.53×10^(8).This substrate has an ultralow detection limit,incurs low processing costs and is simple to prepare;thus,the substrate has significant application potential in the trace analysis field.

Instance Segmentation and Berry Counting of Table Grape before Thinning Based on AS-SwinT

Berry thinning is one of the most important tasks in the management of high-quality table grapes.Farmers often thin the berries per cluster to a standard number by counting.With an aging population,it is hard to find adequate skilled farmers to work during thinning season.It is urgent to design an intelligent berry-thinning machine to avoid exhaustive repetitive labor.A machine vision system that can determine the number of berries removed and locate the berries removed is a challenge for the thinning machine.A method for instance segmentation of berries and berry counting in a single bunch is proposed based on AS-SwinT.In AS-Swin T,Swin Transformer is performed as the backbone to extract the rich characteristics of grape berries.An adaptive feature fusion is introduced to the neck network to sufficiently preserve the underlying features and enhance the detection of small berries.The size of berries in the dataset is statistically analyzed to optimize the anchor scale,and Soft-NMS is used to filter the candidate frames to reduce the missed detection of densely shaded berries.Finally,the proposed method could achieve 65.7 AP^(box),95.0 AP^(box)_(0.5),57 AP^(box)_(s),62.8 AP^(mask)94.3 AP^(mask)_(0.5),48 AP^(mask)_(s),which is markedly superior to Mask R-CNN,Mask Scoring R-CNN,and Cascade Mask R-CNN.Linear regressions between predicted numbers and actual numbers are also developed to verify the precision of the proposed model.RMSE and R^(2)values are 7.13 and 0.95,respectively,which are substantially higher than other models,showing the advantage of the AS-SwinT model in berry counting estimation.