A device for detecting chemical or biochemical substances in fluids for use in an electrochemical camera. The device includes a first carrier having a sensor array with a plurality of electrochemical sensors. A second carrier includes a porous layer having at least one functional region, in which specifically binding capturing molecules are immobilized. The at least one functional region is arranged directly adjacent to at least one non-functionalized region. Assigned to the at least one functional region and the at least one non-functionalized region are several sensors of the sensor array, for use as the electrochemical camera.

Provided are methods and compositions for creating an arrayed library of cells. In particular, the methods and compositions provided herein improve the selection and transfer of cells by reducing contamination, increasing throughput, and improving consistency of transfer. Such methods and compositions include automated colony selection employing a solid barrier mounted on an automated apparatus for automatic handling of one or more pick tools. The solid barrier is configured to prevent undesired transfer of cells from a pick tool into the destination receptacle. Such methods and compositions also include acoustic transfer of cells in a high-density media by an acoustic liquid handler. The methods and compositions further include pooling the cells using a bitcode sample pooling scheme for high-throughput sequencing and deconvoluting the obtained nucleic acid sequences to identify the cells from which the sequences originated.

Provided are methods and compositions for creating an arrayed library of cells. In particular, the methods and compositions provided herein improve the selection and transfer of cells by reducing contamination, increasing throughput, and improving consistency of transfer. Such methods and compositions include automated colony selection employing a solid barrier mounted on an automated apparatus for automatic handling of one or more pick tools. The solid barrier is configured to prevent undesired transfer of cells from a pick tool into the destination receptacle. Such methods and compositions also include acoustic transfer of cells in a high-density media by an acoustic liquid handler. The methods and compositions further include pooling the cells using a bitcode sample pooling scheme for high-throughput sequencing and deconvoluting the obtained nucleic acid sequences to identify the cells from which the sequences originated.

A shower head of a combinatorial spatial atomic layer deposition (CS-ALD) apparatus may be provided. The shower head of the CS-ALD apparatus may include a plurality of shower blocks. Each of shower blocks may include a plurality of unit modules. Each of the shower blocks and each of the unit modules may be controlled independently from each other. Each of the plurality of unit modules may include a source gas injection nozzle, a purge gas injection nozzle, a reactant gas injection nozzle, and exhaust areas between the injection nozzles. The plurality of shower blocks may be separated from each other. Gas injection areas of the injection nozzles may be separated from the exhaust area.

A system, computer program product and a method to predict an objective function based on a recipe, and generate, via a machine learning model, a plurality of proposed different recipes of battery materials for optimizing at least objective function. Instances of the different proposed recipes of battery materials are prepared and deposited into an electrochemical module by a robotic preparation module. A robotic testing module executes a plurality of formulation characteristic tests on each deposited recipe instance and updates the machine learning model with a result of at least one of the formulation characteristic tests.

A system, computer program product and a method to predict an objective function based on a recipe, and generate, via a machine learning model, a plurality of proposed different recipes of battery materials for optimizing at least objective function. Instances of the different proposed recipes of battery materials are prepared and deposited into an electrochemical module by a robotic preparation module. A robotic testing module executes a plurality of formulation characteristic tests on each deposited recipe instance and updates the machine learning model with a result of at least one of the formulation characteristic tests.