The foundation for the pneumatic multi‐material printer was a Creality CR‐10 max printer which originally worked with mechanical filament extrusion. For pneumatic‐extrusion printing of soft materials, two pressure regulators (ITV‐Series, SMC Pneumatics), ITV0010 (positive pressure) and ITV0090 (vacuum) were used. The pressure was provided by the in‐house pressure supply and by a vacuum pump (KNF Laboport PM19627‐86). To control the pressure regulators during printing, a Raspberry Pi 4 Model B was installed that uses Octoprint to communicate with the printer firmware. An Octoprint extension called “Octoprint system commands” detected special commands during gcode execution and was used to run Python scripts by those special commands. The pressure regulators were connected to the Raspberry Pi by a DAC (Adafruit MCP4728) and an ADC converter (Adafruit ADS1115). The additional heating coil of the biogel reservoir was connected to an amplifier circuit that was controlled by the Raspberry Pi's PWM pin. Several individual Python scripts were written to control all additional components. Finally, an awk script was written, that reads gcode files, translates extrusion and retraction commands into pressure settings, translates extruder coordinates according to the active extruder, and inserts the tool switching procedures and special commands for execution of the Python scripts. For faster curing of the printed organic inks, a chamber made of 5 mm PMMA was built around the printer, and a mobile air conditioner (PAC 2100 X TROTEC) was attached to it to control the temperature within the chamber. The workflow was then as follows: A gcode file was uploaded to Octoprint and the awk script ran through the gcode and made changes and inserted the special commands for Python script execution. When the print was started, Ocoprint sent gcode lines to the printer that executes them while “Octoprint system commands” ran the according Python scripts whenever a special command was in the gcode file.
Ads1115
Manufactured by Adafruit
Sourced in United States
About the product
The ADS1115 is a 16-bit analog-to-digital converter (ADC) that can be used to measure voltage levels. It features four differential input channels, a programmable gain amplifier, and a conversion rate of up to 860 samples per second. The ADS1115 communicates via the I2C protocol and can be powered from a wide range of voltages.
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Market Availability & Pricing
The ADS1115 16-Bit ADC - 4 Channel with Programmable Gain Amplifier is currently available from Adafruit and their authorized distributors. The product is priced at $14.95.
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Product FAQ
8 protocols using «ads1115»
1
Pneumatic Multi-Material 3D Printing
2
Streaming Potential Measurement Cell Design
The geometry of the measurement
cell is presented in our earlier papers.10 (link)−12 (link) The cell allows
for the in and out flow of electrolytes, connection to a differential
pressure sensor, and insertion of Ag|AgCl reference electrodes (Figure 1 ). Furthermore, the
cell allows for reverse osmosis netting with 2 × 2 mm grids and
a 2 mm thickness to prevent the membrane from bending when the system
is under pressure. The membrane is secured between the two-halves
of the cell, sealed using 2 mm thick silicone gaskets on either side,
and compressed using M4 bolts in each corner.
A schematic representation of the streaming potential
setup is
shown inFigure 1 .
The operational amplifier circuit used an OPA2191 precision operational
amplifier (Texas Instruments) in an instrumentation amplifier configuration.
The circuit was operated using two 9 V batteries together with a LM78M05
precision +5 V regulator and a L790SCV −5 V regulator (STMicroelectronics)
together with the manufacturer-recommended capacitors for clean power
delivery to the precision operational amplifiers. The voltages from
the amplifier circuit were fed into an ADS 1115 16-bit analog-to-digital
converter (ADC, Adafruit) and an RP-2040 microcontroller running Circuit
Python and read with no further gain. The outputs from the OPA2191s
were level-shifted by 2.5 V using a precision voltage reference (ADR03,
Analog Devices) to prevent negative voltages from being supplied to
the ADS 1115. Suitable gain could be set for each of the experiments
by adjusting the gain resistor in the OPA2191 instrumentation amplifier
configuration. The differential pressure of the system was read using
a 6CF6D differential pressure sensor (Honeywell) and was powered and
read using a NAU7802 24-bit ADC (Adafruit), and the pressure readings
were recorded using an RP-2040 microcontroller.
For calibration
curve data of the pressure sensor, a typical manometer
setup was employed. Voltage calibration was performed using a Keithley
2420 source meter unit, through applying a known current over a precision
1 Ω resistor, and was done to account for resistor mismatches.
Control software (written in Python) both operated an Ismatec peristaltic
pump via a NI-6215 DAQ and the analog control inputs available on
the Ismatec pump and recorded the pressure and voltage measurements
obtained from the RP-2040 microcontroller (Cytron Maker Pi RP2040).
cell is presented in our earlier papers.10 (link)−12 (link) The cell allows
for the in and out flow of electrolytes, connection to a differential
pressure sensor, and insertion of Ag|AgCl reference electrodes (
cell allows for reverse osmosis netting with 2 × 2 mm grids and
a 2 mm thickness to prevent the membrane from bending when the system
is under pressure. The membrane is secured between the two-halves
of the cell, sealed using 2 mm thick silicone gaskets on either side,
and compressed using M4 bolts in each corner.
A schematic representation of the streaming potential
setup is
shown in
The operational amplifier circuit used an OPA2191 precision operational
amplifier (Texas Instruments) in an instrumentation amplifier configuration.
The circuit was operated using two 9 V batteries together with a LM78M05
precision +5 V regulator and a L790SCV −5 V regulator (STMicroelectronics)
together with the manufacturer-recommended capacitors for clean power
delivery to the precision operational amplifiers. The voltages from
the amplifier circuit were fed into an ADS 1115 16-bit analog-to-digital
converter (ADC, Adafruit) and an RP-2040 microcontroller running Circuit
Python and read with no further gain. The outputs from the OPA2191s
were level-shifted by 2.5 V using a precision voltage reference (ADR03,
Analog Devices) to prevent negative voltages from being supplied to
the ADS 1115. Suitable gain could be set for each of the experiments
by adjusting the gain resistor in the OPA2191 instrumentation amplifier
configuration. The differential pressure of the system was read using
a 6CF6D differential pressure sensor (Honeywell) and was powered and
read using a NAU7802 24-bit ADC (Adafruit), and the pressure readings
were recorded using an RP-2040 microcontroller.
For calibration
curve data of the pressure sensor, a typical manometer
setup was employed. Voltage calibration was performed using a Keithley
2420 source meter unit, through applying a known current over a precision
1 Ω resistor, and was done to account for resistor mismatches.
Control software (written in Python) both operated an Ismatec peristaltic
pump via a NI-6215 DAQ and the analog control inputs available on
the Ismatec pump and recorded the pressure and voltage measurements
obtained from the RP-2040 microcontroller (Cytron Maker Pi RP2040).
3
Pneumatic Powered ExHand Exoskeleton
The pneumatic system of the ExHand Exoskeleton is composed of an air pump ROB-10398 (Sparkfun Electronics, USA) of 32 psi of pressure. The ROB-10398 air pump can be used either as a vacuum pump or an air pump; in this case, the air pump is used for the ExHand Exoskeleton. For the selective pressurization of the balloons, a system of 11 solenoid electrovalves (Adafruit, USA) of three ways in two positions is implemented. Thus, 10 electrovalves control the flexion/extension movements performed by the selective pressurization of the internal balloons, and one electrovalve controls the air output. In addition, ten pressure sensors (MPX4250DP, NXP, Netherlands) have been added to measure the air pressure entering each of the inner balloons. Thus, air leakage due to over-pressurization is prevented, and the pressure can be adjusted to the user's requirements. The pneumatic schematic of the ExHand Exoskeleton is presented in Figure 4 .
The control of each internal balloon gives the exoskeleton the ability for the extension balloons to work simultaneously with the flexion balloons; this enables the exoskeleton to perform different combinations resulting in different types of grasp such as power grip, pulp pinch, tripod pinch commonly used in ADL, or actuate each finger separately if needed.Figure 5 shows some configurations.
A web interface was developed for the operation of the exoskeleton; in this interface, different modes of operation are established, for example, the extension of all fingers, different grips such as power grip, pulp pinch or tripod pinch, and depressurization of the system. Also, a configuration panel was added to adjust the pressure limits for each internal balloon as required by the user.
Regarding the electronics system, 3 ADCs (ADS1115, Adafruit, New York, USA) are configured at 860 samples per second to read the pressure sensors' data. In addition, four 4-channel MOSFET switching modules were implemented as electric switches for the air pump and solenoid valves. Thus, as soon as a command is received from the web interface, the air pump, and solenoid valves corresponding to the requested motion are turned on, as shown inFigure 5A or Figure 5B . Once the pressure set by the user is reached, the air pump and solenoid valves are turned off to prevent over-pressurization. In the event of an air leak due to damage to the internal balloons, the air pump and corresponding solenoid valve will be kept until the user sends a different command from the web interface. All the processing and control of the device is performed by one single board computer (Raspberry Pi 3 B+) with the official operating system for Raspberry Pi systems based on Debian, Raspbian OS, and running Robot Operating System (ROS). In terms of consumption, the Raspberry Pi 3 B+ is sufficient to power the ADCs and pressure sensors, as each ADC consumes 5 V/150 μA, and each pressure sensor consumes 5 V/7.0 mA. As for the air pump (12 V, 1 A) and the solenoid valves (5 V, 220 mA), a separate 12 V/5 A power supply and a DC-DC voltage regulator (LM2596, DFRobot, Shanghai, China) set to 5 V are used. Finally, Figure 6 illustrates the electronic system and its connections for clarity.
The control of each internal balloon gives the exoskeleton the ability for the extension balloons to work simultaneously with the flexion balloons; this enables the exoskeleton to perform different combinations resulting in different types of grasp such as power grip, pulp pinch, tripod pinch commonly used in ADL, or actuate each finger separately if needed.
A web interface was developed for the operation of the exoskeleton; in this interface, different modes of operation are established, for example, the extension of all fingers, different grips such as power grip, pulp pinch or tripod pinch, and depressurization of the system. Also, a configuration panel was added to adjust the pressure limits for each internal balloon as required by the user.
Regarding the electronics system, 3 ADCs (ADS1115, Adafruit, New York, USA) are configured at 860 samples per second to read the pressure sensors' data. In addition, four 4-channel MOSFET switching modules were implemented as electric switches for the air pump and solenoid valves. Thus, as soon as a command is received from the web interface, the air pump, and solenoid valves corresponding to the requested motion are turned on, as shown in
4
Low-cost Sensor System for Methane Monitoring
Laboratory calibrations
with gas mixtures at different H2O(g) levels were supplemented
by field tests where the LCSSs were tested in situ toward CH4 levels and other influencing parameters (mainly H2O(g), T, P, and possible interfering gases).
The LCSS consisted of a tailor-made printed circuit board (PCB) that
in this case powered and controlled three TGS sensors (one TGSC and
two TGSE) and one BME680 sensor via an Arduino MKR WAN 1310 from Arduino
AG (Mainz, Germany), a 16-bit analog-to-digital converter ADS1115
from Adafruit Industries (New York, USA), and an in-house C code uploaded using the Visual Studio software and Platform
IO extension. The data acquired by the different sensors was logged
to a secure digital card (SD card) on an MKR SD Proto Shield from
Arduino AG together with the time, date, and geolocalization reported
by the global positioning system (GPS; by an Arduino MKR GPS Shield
from Arduino AG) at 1 min intervals. The LCSSs were powered with 12
or 9 V transformers. For field measurements, the LCSSs were protected
from rain with a housing, made from modified low-cost PE lunch jars
and cutting boards, in which the sensors were directly exposed to
air through a big bottom opening, while the design promoted convective
air movement across the system by several small top openings, as illustrated
inFigure 1 .
Although the sensors in the LCSS were continuously
powered, allowing
1 Hz readings, only one data block per minute was logged on the SD
card. This was done to reduce the log file sizes when measuring for
long periods and as a compromise to simultaneous wireless data transfer
tests (outside of the scope of this study). GPS data were used to
assign each data block with a date, time, latitude, longitude, and
altitude. For every TGS sensor in the LCSS, two values per minute
were logged in the SD card: the mean and standard deviation, which
were calculated from 10 readings (one every 6 s) along 1 min. The
BME680 data logged in the SD card were three values per minute (RH, T, and P) from the reading right before
data storage.
with gas mixtures at different H2O(g) levels were supplemented
by field tests where the LCSSs were tested in situ toward CH4 levels and other influencing parameters (mainly H2O(g), T, P, and possible interfering gases).
The LCSS consisted of a tailor-made printed circuit board (PCB) that
in this case powered and controlled three TGS sensors (one TGSC and
two TGSE) and one BME680 sensor via an Arduino MKR WAN 1310 from Arduino
AG (Mainz, Germany), a 16-bit analog-to-digital converter ADS1115
from Adafruit Industries (New York, USA), and an in-house C code uploaded using the Visual Studio software and Platform
IO extension. The data acquired by the different sensors was logged
to a secure digital card (SD card) on an MKR SD Proto Shield from
Arduino AG together with the time, date, and geolocalization reported
by the global positioning system (GPS; by an Arduino MKR GPS Shield
from Arduino AG) at 1 min intervals. The LCSSs were powered with 12
or 9 V transformers. For field measurements, the LCSSs were protected
from rain with a housing, made from modified low-cost PE lunch jars
and cutting boards, in which the sensors were directly exposed to
air through a big bottom opening, while the design promoted convective
air movement across the system by several small top openings, as illustrated
in
Although the sensors in the LCSS were continuously
powered, allowing
1 Hz readings, only one data block per minute was logged on the SD
card. This was done to reduce the log file sizes when measuring for
long periods and as a compromise to simultaneous wireless data transfer
tests (outside of the scope of this study). GPS data were used to
assign each data block with a date, time, latitude, longitude, and
altitude. For every TGS sensor in the LCSS, two values per minute
were logged in the SD card: the mean and standard deviation, which
were calculated from 10 readings (one every 6 s) along 1 min. The
BME680 data logged in the SD card were three values per minute (RH, T, and P) from the reading right before
data storage.
5
Automated Control of LMPA Resistance
The controller consists of a custom printed circuit board (PCB) that connects a motor drive (drv8871, Adafruit, USA) and a 16‐bit ADC (ADS1115, Adafruit, USA) to a microcontroller. The measurement circuit was powered with 5 V and uses a reference resistance to determine the current ILMPA flowing through the LMPA section. The measured voltage VLMPA in the CVS section can then be used to calculate R LMPA = V LMPA / I LMPA S3 , Supporting Information).
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