Two-Color Amplified Spontaneous Emission from Auger-Suppressed Quantum Dots in Liquids

Colloidal quantum-dot (QD) lasing is normally achieved in close-packed solid-state films, as a high QD volume fraction is required for stimulated emission to outcompete fast Auger decay of optical-gain-active multiexciton states. Here a new type of liquid optical-gain medium is demonstrated, in which compact compositionally-graded QDs (ccg-QDs) that feature strong suppression of Auger decay are liquefied using a small amount of solvent. Transient absorption measurements of ccg-QD liquid suspensions reveal broad-band optical gain spanning a wide spectral range from 560 (green) to 675 nm (red). The gain magnitude is sufficient to realize a two-color amplified spontaneous emission (ASE) at 637 and 594 nm due to the band-edge (1S) and the excited-state (1P) transition, respectively. Importantly, the ASE regime is achieved using quasicontinuous excitation with nanosecond pulses. Furthermore, the ASE is highly stable under prolonged excitation, which stands in contrast to traditional dyes that exhibit strong degradation under identical excitation conditions. These observations point toward a considerable potential of high-density ccg-QD suspensions as liquid, dye-like optical gain media that feature readily achievable spectral tunability and stable operation under intense photoexcitation.

Spatiotemporal analysis of 3D human iPSC-derived neural networks using a 3D multi-electrode array

While there is a growing appreciation of three-dimensional (3D) neural tissues (i.e., hydrogel-based, organoids, and spheroids), shown to improve cellular health and network activity to mirror brain-like activity in vivo, functional assessment using current electrophysiology techniques (e.g., planar multi-electrode arrays or patch clamp) has been technically challenging and limited to surface measurements at the bottom or top of the 3D tissue. As next-generation MEAs, specifically 3D MEAs, are being developed to increase the spatial precision across all three dimensions (X, Y, Z), development of improved computational analytical tools to discern region-specific changes within the Z dimension of the 3D tissue is needed. In the present study, we introduce a novel computational analytical pipeline to analyze 3D neural network activity recorded from a "bottom-up" 3D MEA integrated with a 3D hydrogel-based tissue containing human iPSC-derived neurons and primary astrocytes. Over a period of ~6.5 weeks, we describe the development and maturation of 3D neural activity (i.e., features of spiking and bursting activity) within cross sections of the 3D tissue, based on the vertical position of the electrode on the 3D MEA probe, in addition to network activity (identified using synchrony analysis) within and between cross sections. Then, using the sequential addition of postsynaptic receptor antagonists, bicuculline (BIC), 2-amino-5-phosphonovaleric acid (AP-5), and 6-cyano-5-nitroquinoxaline-2,3-dione (CNQX), we demonstrate that networks within and between cross sections of the 3D hydrogel-based tissue show a preference for GABA and/or glutamate synaptic transmission, suggesting differences in the network composition throughout the neural tissue. The ability to monitor the functional dynamics of the entire 3D reconstructed neural tissue is a critical bottleneck; here we demonstrate a computational pipeline that can be implemented in studies to better interpret network activity within an engineered 3D neural tissue and have a better understanding of the modeled organ tissue.

Global minimization via classical tunneling assisted by collective force field formation

Simple elements interacting in networks can give rise to intricate emergent behaviors. Examples such as synchronization and phase transitions often apply in many contexts, as many different systems may reduce to the same effective model. Here, we demonstrate such a behavior in a model inspired by memristors. When weakly driven, the system is described by movement in an effective potential, but when strongly driven, instabilities cause escapes from local minima, which can be interpreted as an unstable tunneling mechanism. We dub this collective and nonperturbative effect a “Lyapunov force,” which steers the system toward the global minimum of the potential function, even if the full system has a constellation of equilibrium points growing exponentially with the system size. This mechanism is appealing for its physical relevance in nanoscale physics and for its possible applications in optimization, Monte Carlo schemes, and machine learning.