Mechanoluminescent(ML)materials,which have the ability to convert mechanical energy to optical energy,have found huge promising applications such as in stress imaging and anti-counterfeiting.However,the main reported ...Mechanoluminescent(ML)materials,which have the ability to convert mechanical energy to optical energy,have found huge promising applications such as in stress imaging and anti-counterfeiting.However,the main reported ML phosphors are based on trap-related ones,thus hindering the practical applications due to the requirement of complex light pre-irradiation process.Here,a self-recoverable near infrared(NIR)ML material of Lali-xO:xCr^(3+)(x=0.2%,0.4%,0.6%,0.8%,1.0%,and 1.2%)has been developed.Based on the preheating method and corresponding ML performance analysis,the influences of residual carriers are eliminated and the detailed dynamic luminescence process analysis is realized.Systematic experiments are conducted to reveal the origin of the ML emissions,demonstrating that ML is dictated more by the non-centrosymmetric piezoelectric crystal characteristic.In general,this work has provided significant references for exploring more efficient NIR ML materials,which may provide potential applications in anti-counterfeiting and bio-stress sensing.展开更多
Cowpea (Vigna unguiculata L. Walp) is a multi-purpose legume with high quality protein for human consumption and livestock. The objective of this work was to develop near-infrared spectroscopy (NIRS) prediction models...Cowpea (Vigna unguiculata L. Walp) is a multi-purpose legume with high quality protein for human consumption and livestock. The objective of this work was to develop near-infrared spectroscopy (NIRS) prediction models to estimate protein content in cowpea. A total of 116 cowpea breeding lines with a wide range of protein contents (19.28 % to 32.04%) were selected to build the model using whole seed and ground seed samples. Partial least-squares discriminant analysis (PLS-DA) regression technique with different pre-treatments (derivatives, standard normal variate, and multiplicative scatter correction) were carried out to develop the protein prediction model. Results showed: 1) spectral plots of both the whole seed and ground seed showed higher spectral scatter at higher wavelengths (>1450 nm), 2) data pre-processing affects prediction accuracy for bot whole seed and ground seed samples, 3) prediction using ground seed samples (0.64 R<sup>2</sup> 0.85) is better than the whole seed (0.33 R<sup>2</sup> 0.78), and 4) the data pre-processing second derivative with standard normal variate has the best prediction (R<sup>2</sup>_whole seed = 0.78, R<sup>2</sup>_ground seed = 0.85). The results will be of interest in cowpea breeding programs aimed at improving total seed protein content.展开更多
基金We gratefully acknowledge the financial support from the National Natural Science Foundation of China(No.52202003)Guangdong Basic and Applied Basic Research Foundation(No.2023A1515011893)+1 种基金State Key Laboratory of Powder Metallurgy,Central South University,Changsha,China(No.Sklpm-KF-27)Guangzhou Basic and Applied Basic Research Foundation(No.SL2022A04J00746)。
文摘Mechanoluminescent(ML)materials,which have the ability to convert mechanical energy to optical energy,have found huge promising applications such as in stress imaging and anti-counterfeiting.However,the main reported ML phosphors are based on trap-related ones,thus hindering the practical applications due to the requirement of complex light pre-irradiation process.Here,a self-recoverable near infrared(NIR)ML material of Lali-xO:xCr^(3+)(x=0.2%,0.4%,0.6%,0.8%,1.0%,and 1.2%)has been developed.Based on the preheating method and corresponding ML performance analysis,the influences of residual carriers are eliminated and the detailed dynamic luminescence process analysis is realized.Systematic experiments are conducted to reveal the origin of the ML emissions,demonstrating that ML is dictated more by the non-centrosymmetric piezoelectric crystal characteristic.In general,this work has provided significant references for exploring more efficient NIR ML materials,which may provide potential applications in anti-counterfeiting and bio-stress sensing.
文摘Cowpea (Vigna unguiculata L. Walp) is a multi-purpose legume with high quality protein for human consumption and livestock. The objective of this work was to develop near-infrared spectroscopy (NIRS) prediction models to estimate protein content in cowpea. A total of 116 cowpea breeding lines with a wide range of protein contents (19.28 % to 32.04%) were selected to build the model using whole seed and ground seed samples. Partial least-squares discriminant analysis (PLS-DA) regression technique with different pre-treatments (derivatives, standard normal variate, and multiplicative scatter correction) were carried out to develop the protein prediction model. Results showed: 1) spectral plots of both the whole seed and ground seed showed higher spectral scatter at higher wavelengths (>1450 nm), 2) data pre-processing affects prediction accuracy for bot whole seed and ground seed samples, 3) prediction using ground seed samples (0.64 R<sup>2</sup> 0.85) is better than the whole seed (0.33 R<sup>2</sup> 0.78), and 4) the data pre-processing second derivative with standard normal variate has the best prediction (R<sup>2</sup>_whole seed = 0.78, R<sup>2</sup>_ground seed = 0.85). The results will be of interest in cowpea breeding programs aimed at improving total seed protein content.
