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Abstract Unidirectional optical systems enable selective control of light through asymmetric processing of radiation, effectively transmitting light in one direction while blocking unwanted propagation in the opposite direction. Here, a reciprocal diffractive unidirectional focusing design based on linear and isotropic diffractive layers that are structured is introduced. Using gradient descent‐based optimization, a cascaded set of diffractive layers are spatially engineered at the wavelength scale to focus light efficiently in the forward direction while blocking it in the opposite direction. The forward energy focusing efficiency and the backward energy suppression capabilities of this unidirectional architecture are demonstrated under various illumination angles and wavelengths, illustrating the versatility of the polarization‐insensitive design. Furthermore, it is demonstrated that these designs are resilient to adversarial attacks that utilize wavefront engineering from outside. Experimental validation using terahertz radiation confirmed the feasibility of this diffractive unidirectional focusing framework. Diffractive unidirectional designs can operate across different parts of the electromagnetic spectrum by scaling the resulting diffractive features proportional to the wavelength of light and will find applications in security, defense, and optical communication, among others. 

Abstract Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers. Here, we present a computer-free, all-optical image reconstruction method to see through random diffusers at the speed of light. Using deep learning, a set of transmissive diffractive surfaces are trained to all-optically reconstruct images of arbitrary objects that are completely covered by unknown, random phase diffusers. After the training stage, which is a one-time effort, the resulting diffractive surfaces are fabricated and form a passive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown, new phase diffuser. We experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown, random diffusers, never used during training. Unlike digital methods, all-optical diffractive reconstructions do not require power except for the illumination light. This diffractive solution to see through diffusers can be extended to other wavelengths, and might fuel various applications in biomedical imaging, astronomy, atmospheric sciences, oceanography, security, robotics, autonomous vehicles, among many others. 

Abstract Intra‐specific trait variation (ITV) plays a role in processes at a wide range of scales from organs to ecosystems across climate gradients. Yet, ITV remains rarely quantified for many ecophysiological traits typically assessed for species means, such as pressure volume (PV) curve parameters including osmotic potential at full turgor and modulus of elasticity, which are important in plant water relations. We defined a baseline “reference ITV” (ITV_ref) as the variation among fully exposed, mature sun leaves of replicate individuals of a given species grown in similar, well‐watered conditions, representing the conservative sampling design commonly used for species‐level ecophysiological traits. We hypothesized that PV parameters would show low ITV_refrelative to other leaf morphological traits, and that their intraspecific relationships would be similar to those previously established across species and proposed to arise from biophysical constraints. In a database of novel and published PV curves and additional leaf structural traits for 50 diverse species, we found low ITV_reffor PV parameters relative to other morphological traits, and strong intraspecific relationships among PV traits. Simulation modeling showed that conservative ITV_refenables the use of species‐mean PV parameters for scaling up from spectroscopic measurements of leaf water content to enable sensing of leaf water potential.